{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "a1c59f8a"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch import optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# from data_loading import sine_data_generation\n",
    "# from utils import random_generator\n",
    "# from data_loading import MinMaxScaler\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "# from utils import extract_time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "ahJcCrzPQl-k"
   },
   "outputs": [],
   "source": [
    "def train_test_divide (data_x, data_x_hat, data_t, data_t_hat, train_rate = 0.8):\n",
    "  \"\"\" Divide train and test data for both original and synthetic data.\n",
    "  \n",
    "  Args:\n",
    "    - data_x: original data\n",
    "    - data_x_hat: generated data\n",
    "    - data_t: original time\n",
    "    - data_t_hat: generated time\n",
    "    - train_rate: ratio of training data from the original data\n",
    "  \"\"\"\n",
    "  # Divide train/test index (original data)\n",
    "  no = len(data_x)\n",
    "  idx = np.random.permutation(no)\n",
    "  train_idx = idx[:int(no*train_rate)]\n",
    "  test_idx = idx[int(no*train_rate):]\n",
    "    \n",
    "  train_x = [data_x[i] for i in train_idx]\n",
    "  test_x = [data_x[i] for i in test_idx]\n",
    "  train_t = [data_t[i] for i in train_idx]\n",
    "  test_t = [data_t[i] for i in test_idx]      \n",
    "    \n",
    "  # Divide train/test index (synthetic data)\n",
    "  no = len(data_x_hat)\n",
    "  idx = np.random.permutation(no)\n",
    "  train_idx = idx[:int(no*train_rate)]\n",
    "  test_idx = idx[int(no*train_rate):]\n",
    "  \n",
    "  train_x_hat = [data_x_hat[i] for i in train_idx]\n",
    "  test_x_hat = [data_x_hat[i] for i in test_idx]\n",
    "  train_t_hat = [data_t_hat[i] for i in train_idx]\n",
    "  test_t_hat = [data_t_hat[i] for i in test_idx]\n",
    "  \n",
    "  return train_x, train_x_hat, test_x, test_x_hat, train_t, train_t_hat, test_t, test_t_hat\n",
    "\n",
    "\n",
    "def extract_time (data):\n",
    "  \"\"\"Returns Maximum sequence length and each sequence length.\n",
    "  \n",
    "  Args:\n",
    "    - data: original data\n",
    "    \n",
    "  Returns:\n",
    "    - time: extracted time information\n",
    "    - max_seq_len: maximum sequence length\n",
    "  \"\"\"\n",
    "  time = list()\n",
    "  max_seq_len = 0\n",
    "  for i in range(len(data)):\n",
    "    max_seq_len = max(max_seq_len, len(data[i][:,0]))\n",
    "    time.append(len(data[i][:,0]))\n",
    "    \n",
    "  return time, max_seq_len\n",
    "\n",
    "def random_generator (batch_size, z_dim, T_mb, max_seq_len):\n",
    "  \"\"\"Random vector generation.\n",
    "  \n",
    "  Args:\n",
    "    - batch_size: size of the random vector\n",
    "    - z_dim: dimension of random vector\n",
    "    - T_mb: time information for the random vector\n",
    "    - max_seq_len: maximum sequence length\n",
    "    \n",
    "  Returns:\n",
    "    - Z_mb: generated random vector\n",
    "  \"\"\"\n",
    "  Z_mb = list()\n",
    "  for i in range(batch_size):\n",
    "    temp = np.zeros([max_seq_len, z_dim])\n",
    "    temp_Z = np.random.uniform(0., 1, [T_mb[i], z_dim])\n",
    "    temp[:T_mb[i],:] = temp_Z\n",
    "    Z_mb.append(temp_Z)\n",
    "  return Z_mb\n",
    "\n",
    "\n",
    "def batch_generator(data, time, batch_size):\n",
    "  \"\"\"Mini-batch generator.\n",
    "  \n",
    "  Args:\n",
    "    - data: time-series data\n",
    "    - time: time information\n",
    "    - batch_size: the number of samples in each batch\n",
    "    \n",
    "  Returns:\n",
    "    - X_mb: time-series data in each batch\n",
    "    - T_mb: time information in each batch\n",
    "  \"\"\"\n",
    "  no = len(data)\n",
    "  idx = np.random.permutation(no)\n",
    "  train_idx = idx[:batch_size]     \n",
    "            \n",
    "  X_mb = list(data[i] for i in train_idx)\n",
    "  T_mb = list(time[i] for i in train_idx)\n",
    "  \n",
    "  return X_mb, T_mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "KsYQ9_Y1Qe70"
   },
   "outputs": [],
   "source": [
    "def MinMaxScaler(data):\n",
    "  \"\"\"Min Max normalizer.\n",
    "  \n",
    "  Args:\n",
    "    - data: original data\n",
    "  \n",
    "  Returns:\n",
    "    - norm_data: normalized data\n",
    "  \"\"\"\n",
    "  numerator = data - np.min(data, 0)\n",
    "  denominator = np.max(data, 0) - np.min(data, 0)\n",
    "  norm_data = numerator / (denominator + 1e-7)\n",
    "  return norm_data\n",
    "\n",
    "\n",
    "def sine_data_generation (no, seq_len, dim):\n",
    "  \"\"\"Sine data generation.\n",
    "  \n",
    "  Args:\n",
    "    - no: the number of samples\n",
    "    - seq_len: sequence length of the time-series\n",
    "    - dim: feature dimensions\n",
    "    \n",
    "  Returns:\n",
    "    - data: generated data\n",
    "  \"\"\"  \n",
    "  # Initialize the output\n",
    "  data = list()\n",
    "\n",
    "  # Generate sine data\n",
    "  for i in range(no):      \n",
    "    # Initialize each time-series\n",
    "    temp = list()\n",
    "    # For each feature\n",
    "    for k in range(dim):\n",
    "      # Randomly drawn frequency and phase\n",
    "      freq = np.random.uniform(0, 0.1)            \n",
    "      phase = np.random.uniform(0, 0.1)\n",
    "          \n",
    "      # Generate sine signal based on the drawn frequency and phase\n",
    "      temp_data = [np.sin(freq * j + phase) for j in range(seq_len)] \n",
    "      temp.append(temp_data)\n",
    "        \n",
    "    # Align row/column\n",
    "    temp = np.transpose(np.asarray(temp))        \n",
    "    # Normalize to [0,1]\n",
    "    temp = (temp + 1)*0.5\n",
    "    # Stack the generated data\n",
    "    data.append(temp)\n",
    "                \n",
    "  return data\n",
    "    \n",
    "\n",
    "def real_data_loading (data_name, seq_len):\n",
    "  \"\"\"Load and preprocess real-world datasets.\n",
    "  \n",
    "  Args:\n",
    "    - data_name: stock or energy\n",
    "    - seq_len: sequence length\n",
    "    \n",
    "  Returns:\n",
    "    - data: preprocessed data.\n",
    "  \"\"\"  \n",
    "  assert data_name in ['stock','energy']\n",
    "  \n",
    "  if data_name == 'stock':\n",
    "    ori_data = np.loadtxt('data/stock_data.csv', delimiter = \",\",skiprows = 1)\n",
    "  elif data_name == 'energy':\n",
    "    ori_data = np.loadtxt('data/energy_data.csv', delimiter = \",\",skiprows = 1)\n",
    "        \n",
    "  # Flip the data to make chronological data\n",
    "  ori_data = ori_data[::-1]\n",
    "  # Normalize the data\n",
    "  ori_data = MinMaxScaler(ori_data)\n",
    "    \n",
    "  # Preprocess the dataset\n",
    "  temp_data = []    \n",
    "  # Cut data by sequence length\n",
    "  for i in range(0, len(ori_data) - seq_len):\n",
    "    _x = ori_data[i:i + seq_len]\n",
    "    temp_data.append(_x)\n",
    "        \n",
    "  # Mix the datasets (to make it similar to i.i.d)\n",
    "  idx = np.random.permutation(len(temp_data))    \n",
    "  data = []\n",
    "  for i in range(len(temp_data)):\n",
    "    data.append(temp_data[idx[i]])\n",
    "    \n",
    "  return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4e849290"
   },
   "source": [
    "Define Class for Module Construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "294d9abe"
   },
   "outputs": [],
   "source": [
    "class Time_GAN_module(nn.Module):\n",
    "    \"\"\"\n",
    "    Class from which a module of the Time GAN Architecture can be constructed, \n",
    "    consisting of a n_layer stacked RNN layers and a fully connected layer\n",
    "    \n",
    "    input_size = dim of data (depending if module operates on latent or non-latent space)\n",
    "    \"\"\"\n",
    "    def __init__(self, input_size, output_size, hidden_dim, n_layers, activation=torch.sigmoid):\n",
    "        super(Time_GAN_module, self).__init__()\n",
    "\n",
    "        # Parameters\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.n_layers = n_layers\n",
    "        self.sigma = activation\n",
    "\n",
    "        #Defining the layers\n",
    "        # RNN Layer\n",
    "        self.rnn = nn.GRU(input_size, hidden_dim, n_layers, batch_first=True)   \n",
    "        # Fully connected layer\n",
    "        self.fc = nn.Linear(hidden_dim, output_size)\n",
    "        \n",
    "    def forward(self, x):\n",
    "    \n",
    "            batch_size = x.size(0)\n",
    "\n",
    "            # Initializing hidden state for first input using method defined below\n",
    "            hidden = self.init_hidden(batch_size)\n",
    "\n",
    "            # Passing in the input and hidden state into the model and obtaining outputs\n",
    "            out, hidden = self.rnn(x, hidden)\n",
    "        \n",
    "            # Reshaping the outputs such that it can be fit into the fully connected layer\n",
    "            out = out.contiguous().view(-1, self.hidden_dim)\n",
    "            out = self.fc(out)\n",
    "            \n",
    "            if self.sigma == nn.Identity:\n",
    "                idendity = nn.Identity()\n",
    "                return idendity(out)\n",
    "                \n",
    "            out = self.sigma(out)\n",
    "            \n",
    "            # HIDDEN STATES WERDEN IN DER PAPER IMPLEMENTIERUNG AUCH COMPUTED, ALLERDINGS NICHT BENUTZT?\n",
    "            \n",
    "            return out, hidden\n",
    "    \n",
    "    def init_hidden(self, batch_size):\n",
    "        # This method generates the first hidden state of zeros which we'll use in the forward pass\n",
    "        # We'll send the tensor holding the hidden state to the device we specified earlier as well\n",
    "        hidden = torch.zeros(self.n_layers, batch_size, self.hidden_dim)\n",
    "        return hidden"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "65ad1d5e"
   },
   "source": [
    "Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "6a2e3f85"
   },
   "outputs": [],
   "source": [
    "input_size = 5 # sequence length = number of features\n",
    "output_size = 20\n",
    "hidden_dim = 20\n",
    "n_layers = 3\n",
    "gamma = 1\n",
    "\n",
    "no, seq_len, dim = 12800, 24, 5 \n",
    "\n",
    "batch_size = 128\n",
    "epoch = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b35b74eb"
   },
   "source": [
    "Data Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2373fb48",
    "outputId": "00332056-d68c-4eb0-e28c-a2df411f706f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([12800, 24, 5])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sine_data_generation(no, seq_len, dim)\n",
    "data = MinMaxScaler(data)\n",
    "data = torch.Tensor(data)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "90e9892c"
   },
   "source": [
    "Create Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "nVra-i_jieoc"
   },
   "outputs": [],
   "source": [
    "# embedder: num_layers = num_layers, fully_connected dim = hidden_dim\n",
    "# recovery: num_layers = num_layers, fully_connected dim = dim \n",
    "# generator: num layers = num_layers, fully_connected dim = hidden_dim\n",
    "# supervisor: num_layers = num_layers-1, fully_connected dim = hidden_dim\n",
    "# discriminator: num_layers = num_layers, fully_connected dim = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "NU73wgsajAtM"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "db4e7203",
    "outputId": "ef492e6d-288a-4490-fed9-c1de8efa01e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time_GAN_module(\n",
       "  (rnn): GRU(5, 20, num_layers=3, batch_first=True)\n",
       "  (fc): Linear(in_features=20, out_features=20, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Embedder = Time_GAN_module(input_size=dim, output_size=hidden_dim, hidden_dim=hidden_dim, n_layers=n_layers)\n",
    "Embedder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9edcdcda",
    "outputId": "229d16fe-0033-4aac-bc4b-b76259aab192"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time_GAN_module(\n",
       "  (rnn): GRU(20, 20, num_layers=3, batch_first=True)\n",
       "  (fc): Linear(in_features=20, out_features=5, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Recovery = Time_GAN_module(input_size=hidden_dim, output_size=dim, hidden_dim=hidden_dim, n_layers=n_layers)\n",
    "Recovery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "700d9516",
    "outputId": "9cba92f8-a7d6-47af-fd4b-2bb8ba88ff2f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time_GAN_module(\n",
       "  (rnn): GRU(5, 20, num_layers=3, batch_first=True)\n",
       "  (fc): Linear(in_features=20, out_features=20, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Generator = Time_GAN_module(input_size=dim, output_size=hidden_dim, hidden_dim=hidden_dim, n_layers=n_layers)\n",
    "Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "771283fc",
    "outputId": "8d5fc441-0979-4088-99f6-5689a6fe8129"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time_GAN_module(\n",
       "  (rnn): GRU(20, 20, num_layers=2, batch_first=True)\n",
       "  (fc): Linear(in_features=20, out_features=20, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Supervisor = Time_GAN_module(input_size=hidden_dim, output_size=hidden_dim, hidden_dim=hidden_dim, n_layers=n_layers-1)\n",
    "Supervisor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "03e296b8",
    "outputId": "ad523d22-e431-443b-a6e1-d46f9594b56b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time_GAN_module(\n",
       "  (rnn): GRU(20, 20, num_layers=3, batch_first=True)\n",
       "  (fc): Linear(in_features=20, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Discriminator = Time_GAN_module(input_size=hidden_dim, output_size=1, hidden_dim=hidden_dim, n_layers=n_layers, \n",
    "                               activation=nn.Identity)\n",
    "Discriminator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a27f997b"
   },
   "source": [
    "Create Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "c7aed175"
   },
   "outputs": [],
   "source": [
    "embedder_optimizer = optim.Adam(Embedder.parameters(), lr=0.001)\n",
    "recovery_optimizer = optim.Adam(Recovery.parameters(), lr=0.001)\n",
    "supervisor_optimizer = optim.Adam(Recovery.parameters(), lr=0.001)\n",
    "discriminator_optimizer = optim.Adam(Discriminator.parameters(), lr=0.001)\n",
    "generator_optimizer = optim.Adam(Generator.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a05ad143"
   },
   "source": [
    "Data Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f04458be",
    "outputId": "e1153625-f0ed-4135-c3de-5ea2b1918fd1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([3072, 20]), torch.Size([128, 24, 20]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loader = DataLoader(data, batch_size, shuffle=True)\n",
    "X = next(iter(loader))\n",
    "H, _ = Embedder(X.float())\n",
    "H_re = torch.reshape(H, (batch_size, seq_len, hidden_dim))\n",
    "H.shape, H_re.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xVFi9yxqeDqL",
    "outputId": "505cf277-e768-445b-c616-7fc03eeb8c3a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 24, 20)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size, seq_len, hidden_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "9e2cd6ad"
   },
   "outputs": [],
   "source": [
    "random_data = random_generator(batch_size=batch_size, z_dim=dim, \n",
    "                                       T_mb=extract_time(data)[0], max_seq_len=extract_time(data)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "627dd17f"
   },
   "outputs": [],
   "source": [
    "loader = DataLoader(data, batch_size, shuffle=True)\n",
    "\n",
    "random_loader = DataLoader(random_data, batch_size, shuffle=True)\n",
    "\n",
    "binary_cross_entropy_loss = nn.BCEWithLogitsLoss()\n",
    "\n",
    "MSE_loss = nn.MSELoss()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3i8UwTo2kuXi",
    "outputId": "8c39f349-3db7-4616-ce01-ec79f8d7505e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([128, 24, 5])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = next(iter(loader))\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "id": "GFKdBugWo9OM"
   },
   "outputs": [],
   "source": [
    "def TimeGAN(data, parameters):\n",
    "  hidden_dim = parameters[\"hidden_dim\"]\n",
    "  num_layers = parameters[\"num_layers\"]\n",
    "  iterations = parameters[\"iterations\"]\n",
    "  batch_size = parameters[\"batch_size\"]\n",
    "  module = parameters[\"module\"]\n",
    "  epoch = parameters[\"epoch\"]\n",
    "  no, seq_len, dim = np.asarray(data).shape\n",
    "  z_dim = dim\n",
    "  gamma = 1\n",
    "\n",
    "  Embedder = Time_GAN_module(input_size=z_dim, output_size=hidden_dim, hidden_dim=hidden_dim, n_layers=num_layers)\n",
    "  Recovery = Time_GAN_module(input_size=hidden_dim, output_size=dim, hidden_dim=hidden_dim, n_layers=n_layers)\n",
    "  Generator = Time_GAN_module(input_size=dim, output_size=hidden_dim, hidden_dim=hidden_dim, n_layers=n_layers)\n",
    "  Supervisor = Time_GAN_module(input_size=hidden_dim, output_size=hidden_dim, hidden_dim=hidden_dim, n_layers=n_layers-1)\n",
    "  Discriminator = Time_GAN_module(input_size=hidden_dim, output_size=1, hidden_dim=hidden_dim, n_layers=n_layers, activation=nn.Identity)\n",
    "\n",
    "  embedder_optimizer = optim.Adam(Embedder.parameters(), lr=0.001)\n",
    "  recovery_optimizer = optim.Adam(Recovery.parameters(), lr=0.001)\n",
    "  supervisor_optimizer = optim.Adam(Recovery.parameters(), lr=0.001)\n",
    "  discriminator_optimizer = optim.Adam(Discriminator.parameters(), lr=0.001)\n",
    "  generator_optimizer = optim.Adam(Generator.parameters(), lr=0.001)\n",
    "\n",
    "  # Embedding Network Training\n",
    "  print('Start Embedding Network Training')\n",
    "  for e in range(epoch): \n",
    "    for batch_index, X in enumerate(loader):\n",
    "        \n",
    "        MSE_loss = nn.MSELoss()\n",
    "        \n",
    "        H, _ = Embedder(X.float())\n",
    "        H = torch.reshape(H, (batch_size, seq_len, hidden_dim))\n",
    "\n",
    "        X_tilde, _ = Recovery(H)\n",
    "        X_tilde = torch.reshape(X_tilde, (batch_size, seq_len, dim))\n",
    "\n",
    "        E_loss0 = 10 * torch.sqrt(MSE_loss(X, X_tilde))  \n",
    "\n",
    "        Embedder.zero_grad()\n",
    "        Recovery.zero_grad()\n",
    "\n",
    "        E_loss0.backward(retain_graph=True)\n",
    "\n",
    "        embedder_optimizer.step()\n",
    "        recovery_optimizer.step()\n",
    "\n",
    "        if e in range(1,epoch) and batch_index == 0:\n",
    "            print('step: '+ str(e) + '/' + str(epoch) + ', e_loss: ' + str(np.sqrt(E_loss0.detach().numpy())))\n",
    "\n",
    "  print('Finish Embedding Network Training')\n",
    "\n",
    "  # Training only with supervised loss\n",
    "  print('Start Training with Supervised Loss Only')\n",
    "  for e in range(epoch): \n",
    "    for batch_index, X in enumerate(loader):\n",
    "\n",
    "        H, _ = Embedder(X.float())\n",
    "        H = torch.reshape(H, (batch_size, seq_len, hidden_dim))\n",
    "\n",
    "        H_hat_supervise, _ = Supervisor(H)\n",
    "        H_hat_supervise = torch.reshape(H_hat_supervise, (batch_size, seq_len, hidden_dim))  \n",
    "\n",
    "        G_loss_S = MSE_loss(H[:,1:,:], H_hat_supervise[:,:-1,:])\n",
    "\n",
    "\n",
    "        Embedder.zero_grad()\n",
    "        Supervisor.zero_grad()\n",
    "\n",
    "        G_loss_S.backward(retain_graph=True)\n",
    "\n",
    "        embedder_optimizer.step()\n",
    "        supervisor_optimizer.step()\n",
    "\n",
    "        if e in range(1,epoch) and batch_index == 0:\n",
    "            print('step: '+ str(e) + '/' + str(epoch) + ', s_loss: ' + str(np.sqrt(G_loss_S.detach().numpy())))\n",
    "\n",
    "  print('Finish Training with Supervised Loss Only')\n",
    "  # Joint Training\n",
    "  \n",
    "\n",
    "  print('Start Joint Training')\n",
    "  for itt in range(epoch):\n",
    "    for kk in range(2):\n",
    "      X = next(iter(loader))\n",
    "      random_data = random_generator(batch_size=batch_size, z_dim=dim, \n",
    "                                       T_mb=extract_time(data)[0], max_seq_len=extract_time(data)[1])\n",
    "        \n",
    "      # Generator Training \n",
    "      ## Train Generator\n",
    "      z = torch.tensor(random_data)\n",
    "      z = z.float()\n",
    "        \n",
    "      e_hat, _ = Generator(z)\n",
    "      e_hat = torch.reshape(e_hat, (batch_size, seq_len, hidden_dim))\n",
    "        \n",
    "      H_hat, _ = Supervisor(e_hat)\n",
    "      H_hat = torch.reshape(H_hat, (batch_size, seq_len, hidden_dim))\n",
    "        \n",
    "      Y_fake = Discriminator(H_hat)\n",
    "      Y_fake = torch.reshape(Y_fake, (batch_size, seq_len, 1))\n",
    "        \n",
    "      x_hat, _ = Recovery(H_hat)\n",
    "      x_hat = torch.reshape(x_hat, (batch_size, seq_len, dim))\n",
    "        \n",
    "      H, _ = Embedder(X.float())\n",
    "      H = torch.reshape(H, (batch_size, seq_len, hidden_dim))\n",
    "\n",
    "      H_hat_supervise, _ = Supervisor(H)\n",
    "      H_hat_supervise = torch.reshape(H_hat_supervise, (batch_size, seq_len, hidden_dim))\n",
    "\n",
    "      Generator.zero_grad()\n",
    "      Supervisor.zero_grad()\n",
    "      Discriminator.zero_grad()\n",
    "      Recovery.zero_grad()\n",
    "\n",
    "      # line 267 of original implementation: \n",
    "      # G_loss_U, G_loss_S, G_loss_V\n",
    "      G_loss_S = MSE_loss(H[:,1:,:], H_hat_supervise[:,:-1,:])\n",
    "      binary_cross_entropy_loss = nn.BCEWithLogitsLoss()\n",
    "      G_loss_U = binary_cross_entropy_loss(torch.ones_like(Y_fake), Y_fake)\n",
    "        \n",
    "      G_loss_V1 = torch.mean(torch.abs((torch.std(x_hat, [0], unbiased = False)) + 1e-6 - (torch.std(X, [0]) + 1e-6)))\n",
    "      G_loss_V2 = torch.mean(torch.abs((torch.mean(x_hat, [0]) - (torch.mean(X, [0])))))\n",
    "      G_loss_V = G_loss_V1 + G_loss_V2\n",
    "        \n",
    "      # doing a backward step for each loss should result in gradients accumulating \n",
    "      # so we should be able to optimize them jointly\n",
    "      G_loss_S.backward(retain_graph=True)#\n",
    "      G_loss_U.backward(retain_graph=True)\n",
    "      G_loss_V.backward(retain_graph=True)#\n",
    "\n",
    "\n",
    "      generator_optimizer.step()\n",
    "      supervisor_optimizer.step()\n",
    "      discriminator_optimizer.step()\n",
    "      # Train Embedder \n",
    "      ## line 270: we only optimize E_loss_T0\n",
    "      ## E_loss_T0 = just mse of x and x_tilde\n",
    "      # but it calls E_solver which optimizes E_loss, which is a sum of \n",
    "      # E_loss0 and 0.1* G_loss_S\n",
    "      MSE_loss = nn.MSELoss()\n",
    "        \n",
    "      H, _ = Embedder(X.float())\n",
    "      H = torch.reshape(H, (batch_size, seq_len, hidden_dim))\n",
    "\n",
    "      X_tilde, _ = Recovery(H)\n",
    "      X_tilde = torch.reshape(X_tilde, (batch_size, seq_len, dim))\n",
    "\n",
    "      E_loss_T0 = MSE_loss(X, X_tilde)\n",
    "      E_loss0 = 10 * torch.sqrt(MSE_loss(X, X_tilde))  \n",
    "        \n",
    "      H_hat_supervise, _ = Supervisor(H)\n",
    "      H_hat_supervise = torch.reshape(H_hat_supervise, (batch_size, seq_len, hidden_dim))  \n",
    "\n",
    "      G_loss_S = MSE_loss(H[:,1:,:], H_hat_supervise[:,:-1,:])\n",
    "      E_loss = E_loss0  + 0.1 * G_loss_S\n",
    "        \n",
    "      G_loss_S.backward(retain_graph=True)\n",
    "      E_loss_T0.backward()\n",
    "        \n",
    "      Embedder.zero_grad()\n",
    "      Recovery.zero_grad()\n",
    "      Supervisor.zero_grad()\n",
    "        \n",
    "      embedder_optimizer.step()\n",
    "      recovery_optimizer.step()\n",
    "      supervisor_optimizer.step()\n",
    "    # train Discriminator\n",
    "    for batch_index, X in enumerate(loader):\n",
    "      random_data = random_generator(batch_size=batch_size, z_dim=dim, \n",
    "                                       T_mb=extract_time(data)[0], max_seq_len=extract_time(data)[1])\n",
    "      \n",
    "      z = torch.tensor(random_data)\n",
    "      z = z.float()\n",
    "\n",
    "      H, _ = Embedder(X)\n",
    "      H = torch.reshape(H, (batch_size, seq_len, hidden_dim))\n",
    "\n",
    "      Y_real = Discriminator(H)\n",
    "      Y_real = torch.reshape(Y_real, (batch_size, seq_len, 1))\n",
    "      \n",
    "      e_hat, _ = Generator(z)\n",
    "      e_hat = torch.reshape(e_hat, (batch_size, seq_len, hidden_dim))\n",
    "\n",
    "      Y_fake_e = Discriminator(e_hat)\n",
    "      Y_fake_e = torch.reshape(Y_fake_e, (batch_size, seq_len, 1))\n",
    "        \n",
    "      H_hat, _ = Supervisor(e_hat)\n",
    "      H_hat = torch.reshape(H_hat, (batch_size, seq_len, hidden_dim))\n",
    "        \n",
    "      Y_fake = Discriminator(H_hat)\n",
    "      Y_fake = torch.reshape(Y_fake, (batch_size, seq_len, 1))\n",
    "        \n",
    "      x_hat, _ = Recovery(H_hat)\n",
    "      x_hat = torch.reshape(x_hat, (batch_size, seq_len, dim))\n",
    "\n",
    "      Generator.zero_grad()\n",
    "      Supervisor.zero_grad()\n",
    "      Discriminator.zero_grad()\n",
    "      Recovery.zero_grad()\n",
    "      Embedder.zero_grad()\n",
    "\n",
    "      # logits first, then targets\n",
    "      # D_loss_real(Y_real, torch.ones_like(Y_real))\n",
    "      D_loss_real = nn.BCEWithLogitsLoss()\n",
    "      DLR = D_loss_real(Y_real, torch.ones_like(Y_real))\n",
    "\n",
    "      D_loss_fake = nn.BCEWithLogitsLoss()\n",
    "      DLF = D_loss_fake(Y_fake, torch.zeros_like(Y_fake))\n",
    "\n",
    "      D_loss_fake_e = nn.BCEWithLogitsLoss()\n",
    "      DLF_e = D_loss_fake_e(Y_fake_e, torch.zeros_like(Y_fake_e))\n",
    "\n",
    "      D_loss = DLR + DLF + gamma * DLF_e\n",
    "\n",
    "      # D_loss.backward(retain_graph=True)\n",
    "      \n",
    "      # discriminator_optimizer.step()\n",
    "\n",
    "      # check discriminator loss before updating\n",
    "      check_d_loss = D_loss\n",
    "      if (check_d_loss > 0.15):\n",
    "        D_loss.backward(retain_graph=True)\n",
    "        discriminator_optimizer.step()        \n",
    "        \n",
    "      H, _ = Embedder(X.float())\n",
    "      H = torch.reshape(H, (batch_size, seq_len, hidden_dim)) \n",
    "        \n",
    "      X_tilde, _ = Recovery(H)\n",
    "      X_tilde = torch.reshape(X_tilde, (batch_size, seq_len, dim))\n",
    "\n",
    "      \n",
    "      z = torch.tensor(random_data)\n",
    "      z = z.float()\n",
    "        \n",
    "      e_hat, _ = Generator(z)\n",
    "      e_hat = torch.reshape(e_hat, (batch_size, seq_len, hidden_dim))\n",
    "        \n",
    "      H_hat, _ = Supervisor(e_hat)\n",
    "      H_hat = torch.reshape(H_hat, (batch_size, seq_len, hidden_dim))\n",
    "        \n",
    "      Y_fake = Discriminator(H_hat)\n",
    "      Y_fake = torch.reshape(Y_fake, (batch_size, seq_len, 1))\n",
    "        \n",
    "      x_hat, _ = Recovery(H_hat)\n",
    "      x_hat = torch.reshape(x_hat, (batch_size, seq_len, dim))\n",
    "        \n",
    "      H, _ = Embedder(X.float())\n",
    "      H = torch.reshape(H, (batch_size, seq_len, hidden_dim))\n",
    "\n",
    "      H_hat_supervise, _ = Supervisor(H)\n",
    "      H_hat_supervise = torch.reshape(H_hat_supervise, (batch_size, seq_len, hidden_dim))\n",
    "\n",
    "      G_loss_S = MSE_loss(H[:,1:,:], H_hat_supervise[:,:-1,:])\n",
    "      binary_cross_entropy_loss = nn.BCEWithLogitsLoss()\n",
    "      G_loss_U = binary_cross_entropy_loss(torch.ones_like(Y_fake), Y_fake)\n",
    "        \n",
    "      G_loss_V1 = torch.mean(torch.abs((torch.std(x_hat, [0], unbiased = False)) + 1e-6 - (torch.std(X, [0]) + 1e-6)))\n",
    "      G_loss_V2 = torch.mean(torch.abs((torch.mean(x_hat, [0]) - (torch.mean(X, [0])))))\n",
    "      G_loss_V = G_loss_V1 + G_loss_V2\n",
    "    \n",
    "      E_loss_T0 = MSE_loss(X, X_tilde)\n",
    "      E_loss0 = 10 * torch.sqrt(MSE_loss(X, X_tilde))  \n",
    "      E_loss = E_loss0  + 0.1 * G_loss_S\n",
    "        \n",
    "      # doing a backward step for each loss should result in gradients accumulating \n",
    "      # so we should be able to optimize them jointly\n",
    "      G_loss_S.backward(retain_graph=True)#\n",
    "      G_loss_U.backward(retain_graph=True)\n",
    "      G_loss_V.backward(retain_graph=True)#\n",
    "      E_loss.backward()\n",
    "\n",
    "      generator_optimizer.step()\n",
    "      supervisor_optimizer.step()\n",
    "      #discriminator_optimizer.step() \n",
    "      embedder_optimizer.step()\n",
    "      recovery_optimizer.step()\n",
    "            \n",
    "      print('step: '+ str(itt) + '/' + str(epoch) + \n",
    "            ', D_loss: ' + str(D_loss.detach().numpy()) +\n",
    "            ', G_loss_U: ' + str(G_loss_U.detach().numpy()) + \n",
    "            ', G_loss_S: ' + str(G_loss_S.detach().numpy()) + \n",
    "            ', E_loss_t0: ' + str(np.sqrt(E_loss0.detach().numpy()))\n",
    "             )\n",
    "  print('Finish Joint Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "id": "YPbbUH0dJqjt"
   },
   "outputs": [],
   "source": [
    "parameters = dict()\n",
    "parameters['module'] = 'gru' \n",
    "parameters['hidden_dim'] = 24\n",
    "parameters['num_layers'] = 3\n",
    "parameters['iterations'] = 10000\n",
    "parameters['batch_size'] = 128\n",
    "parameters['epoch'] = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "9rnug7qqmddM",
    "outputId": "aacf2bb0-7f70-496a-d98f-0316b540c67a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start Embedding Network Training\n",
      "step: 1/50, e_loss: 1.6149828\n",
      "step: 2/50, e_loss: 1.5064645\n",
      "step: 3/50, e_loss: 1.4697814\n",
      "step: 4/50, e_loss: 1.4237516\n",
      "step: 5/50, e_loss: 1.3381127\n",
      "step: 6/50, e_loss: 1.3051033\n",
      "step: 7/50, e_loss: 1.2860656\n",
      "step: 8/50, e_loss: 1.1078863\n",
      "step: 9/50, e_loss: 1.0635854\n",
      "step: 10/50, e_loss: 0.9845366\n",
      "step: 11/50, e_loss: 0.7573251\n",
      "step: 12/50, e_loss: 0.6505333\n",
      "step: 13/50, e_loss: 0.5831463\n",
      "step: 14/50, e_loss: 0.54910254\n",
      "step: 15/50, e_loss: 0.5291148\n",
      "step: 16/50, e_loss: 0.5005566\n",
      "step: 17/50, e_loss: 0.4828723\n",
      "step: 18/50, e_loss: 0.48521358\n",
      "step: 19/50, e_loss: 0.46938503\n",
      "step: 20/50, e_loss: 0.4582306\n",
      "step: 21/50, e_loss: 0.43810058\n",
      "step: 22/50, e_loss: 0.42440876\n",
      "step: 23/50, e_loss: 0.41973054\n",
      "step: 24/50, e_loss: 0.41862756\n",
      "step: 25/50, e_loss: 0.40386507\n",
      "step: 26/50, e_loss: 0.41072008\n",
      "step: 27/50, e_loss: 0.40251216\n",
      "step: 28/50, e_loss: 0.40172195\n",
      "step: 29/50, e_loss: 0.39030847\n",
      "step: 30/50, e_loss: 0.38272506\n",
      "step: 31/50, e_loss: 0.38412166\n",
      "step: 32/50, e_loss: 0.37875274\n",
      "step: 33/50, e_loss: 0.3825963\n",
      "step: 34/50, e_loss: 0.3803958\n",
      "step: 35/50, e_loss: 0.3602068\n",
      "step: 36/50, e_loss: 0.35752442\n",
      "step: 37/50, e_loss: 0.37582418\n",
      "step: 38/50, e_loss: 0.36628252\n",
      "step: 39/50, e_loss: 0.36794668\n",
      "step: 40/50, e_loss: 0.3530521\n",
      "step: 41/50, e_loss: 0.35103408\n",
      "step: 42/50, e_loss: 0.35595804\n",
      "step: 43/50, e_loss: 0.36350128\n",
      "step: 44/50, e_loss: 0.3484893\n",
      "step: 45/50, e_loss: 0.36444357\n",
      "step: 46/50, e_loss: 0.33573422\n",
      "step: 47/50, e_loss: 0.340676\n",
      "step: 48/50, e_loss: 0.34585026\n",
      "step: 49/50, e_loss: 0.33996847\n",
      "Finish Embedding Network Training\n",
      "Start Training with Supervised Loss Only\n",
      "step: 1/50, s_loss: 0.109279245\n",
      "step: 2/50, s_loss: 0.104914464\n",
      "step: 3/50, s_loss: 0.10190782\n",
      "step: 4/50, s_loss: 0.09380689\n",
      "step: 5/50, s_loss: 0.092683434\n",
      "step: 6/50, s_loss: 0.08506773\n",
      "step: 7/50, s_loss: 0.08198868\n",
      "step: 8/50, s_loss: 0.07560783\n",
      "step: 9/50, s_loss: 0.07478305\n",
      "step: 10/50, s_loss: 0.07004512\n",
      "step: 11/50, s_loss: 0.064333074\n",
      "step: 12/50, s_loss: 0.059525054\n",
      "step: 13/50, s_loss: 0.057894155\n",
      "step: 14/50, s_loss: 0.05677229\n",
      "step: 15/50, s_loss: 0.051189817\n",
      "step: 16/50, s_loss: 0.046849463\n",
      "step: 17/50, s_loss: 0.045605563\n",
      "step: 18/50, s_loss: 0.041683443\n",
      "step: 19/50, s_loss: 0.03995734\n",
      "step: 20/50, s_loss: 0.037472\n",
      "step: 21/50, s_loss: 0.035888527\n",
      "step: 22/50, s_loss: 0.03149382\n",
      "step: 23/50, s_loss: 0.03200133\n",
      "step: 24/50, s_loss: 0.030760514\n",
      "step: 25/50, s_loss: 0.028981173\n",
      "step: 26/50, s_loss: 0.029283723\n",
      "step: 27/50, s_loss: 0.027180728\n",
      "step: 28/50, s_loss: 0.026744297\n",
      "step: 29/50, s_loss: 0.024108795\n",
      "step: 30/50, s_loss: 0.024064101\n",
      "step: 31/50, s_loss: 0.02258741\n",
      "step: 32/50, s_loss: 0.021584077\n",
      "step: 33/50, s_loss: 0.021266798\n",
      "step: 34/50, s_loss: 0.020619852\n",
      "step: 35/50, s_loss: 0.020070415\n",
      "step: 36/50, s_loss: 0.018664388\n",
      "step: 37/50, s_loss: 0.018920315\n",
      "step: 38/50, s_loss: 0.018314669\n",
      "step: 39/50, s_loss: 0.017724687\n",
      "step: 40/50, s_loss: 0.016521689\n",
      "step: 41/50, s_loss: 0.015988516\n",
      "step: 42/50, s_loss: 0.015009019\n",
      "step: 43/50, s_loss: 0.015256087\n",
      "step: 44/50, s_loss: 0.014849265\n",
      "step: 45/50, s_loss: 0.013851696\n",
      "step: 46/50, s_loss: 0.013394697\n",
      "step: 47/50, s_loss: 0.013394922\n",
      "step: 48/50, s_loss: 0.013441405\n",
      "step: 49/50, s_loss: 0.013105223\n",
      "Finish Training with Supervised Loss Only\n",
      "Start Joint Training\n",
      "step: 0/50, D_loss: 2.0869966, G_loss_U: 1.2735711, G_loss_S: 0.00014934623, E_loss_t0: 2.5414624\n",
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    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "step: 49/50, D_loss: 0.14988256, G_loss_U: 3.4213378, G_loss_S: 4.798006e-05, E_loss_t0: 2.4531546\n",
      "step: 49/50, D_loss: 0.14988282, G_loss_U: 3.4213371, G_loss_S: 4.7254533e-05, E_loss_t0: 2.5059457\n",
      "step: 49/50, D_loss: 0.14990371, G_loss_U: 3.421337, G_loss_S: 4.8622638e-05, E_loss_t0: 2.4989092\n",
      "step: 49/50, D_loss: 0.1498951, G_loss_U: 3.4213364, G_loss_S: 4.834009e-05, E_loss_t0: 2.471539\n",
      "step: 49/50, D_loss: 0.14990163, G_loss_U: 3.421336, G_loss_S: 4.925183e-05, E_loss_t0: 2.4605858\n",
      "step: 49/50, D_loss: 0.14989333, G_loss_U: 3.421335, G_loss_S: 4.9443956e-05, E_loss_t0: 2.4465\n",
      "step: 49/50, D_loss: 0.1498881, G_loss_U: 3.421334, G_loss_S: 4.8605212e-05, E_loss_t0: 2.4520562\n",
      "step: 49/50, D_loss: 0.149896, G_loss_U: 3.421334, G_loss_S: 4.8283186e-05, E_loss_t0: 2.4530687\n",
      "step: 49/50, D_loss: 0.14990024, G_loss_U: 3.4213333, G_loss_S: 4.8164933e-05, E_loss_t0: 2.4789975\n",
      "step: 49/50, D_loss: 0.14988744, G_loss_U: 3.4213326, G_loss_S: 4.7275076e-05, E_loss_t0: 2.4823468\n",
      "step: 49/50, D_loss: 0.14990464, G_loss_U: 3.4213324, G_loss_S: 4.8668684e-05, E_loss_t0: 2.4450574\n",
      "step: 49/50, D_loss: 0.14990479, G_loss_U: 3.4213316, G_loss_S: 4.7949776e-05, E_loss_t0: 2.484108\n",
      "Finish Joint Training\n"
     ]
    }
   ],
   "source": [
    "TimeGAN(data, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import extract_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_test = random_generator(12800, dim, extract_time(data)[0], extract_time(data)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([12800, 24, 5])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.tensor(random_test).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oben wird die selbe anzahl an Noise-Daten wie in den echten Daten erzeugt, und dann in den Generator geworfen; Das wird dann wiederrum in die Recovery gegeben, und darauf dann die Visualisierung gemacht"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "test = Generator(random_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = Generator(torch.tensor(random_generator(12800, dim, extract_time(data)[0], extract_time(data)[1])).float())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = torch.reshape(test, (12800, seq_len, hidden_dim))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([12800, 24, 20])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_recovery = Recovery(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_recovery = torch.reshape(test_recovery[0], (12800, seq_len, dim))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([12800, 24, 5])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_recovery.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Time-series Generative Adversarial Networks (TimeGAN) Codebase.\n",
    "Reference: Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar, \n",
    "\"Time-series Generative Adversarial Networks,\" \n",
    "Neural Information Processing Systems (NeurIPS), 2019.\n",
    "Paper link: https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks\n",
    "Last updated Date: April 24th 2020\n",
    "Code author: Jinsung Yoon (jsyoon0823@gmail.com)\n",
    "-----------------------------\n",
    "visualization_metrics.py\n",
    "Note: Use PCA or tSNE for generated and original data visualization\n",
    "\"\"\"\n",
    "\n",
    "# Necessary packages\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "   \n",
    "def visualization (ori_data, generated_data, analysis):\n",
    "  \"\"\"Using PCA or tSNE for generated and original data visualization.\n",
    "  \n",
    "  Args:\n",
    "    - ori_data: original data\n",
    "    - generated_data: generated synthetic data\n",
    "    - analysis: tsne or pca\n",
    "  \"\"\"  \n",
    "  # Analysis sample size (for faster computation)\n",
    "  anal_sample_no = min([1000, len(ori_data)])\n",
    "  idx = np.random.permutation(len(ori_data))[:anal_sample_no]\n",
    "    \n",
    "  # Data preprocessing\n",
    "  ori_data = np.asarray(ori_data)\n",
    "  generated_data = np.asarray(generated_data)  \n",
    "  \n",
    "  ori_data = ori_data[idx]\n",
    "  generated_data = generated_data[idx]\n",
    "  \n",
    "  no, seq_len, dim = ori_data.shape  \n",
    "  \n",
    "  for i in range(anal_sample_no):\n",
    "    if (i == 0):\n",
    "      prep_data = np.reshape(np.mean(ori_data[0,:,:], 1), [1,seq_len])\n",
    "      prep_data_hat = np.reshape(np.mean(generated_data[0,:,:],1), [1,seq_len])\n",
    "    else:\n",
    "      prep_data = np.concatenate((prep_data, \n",
    "                                  np.reshape(np.mean(ori_data[i,:,:],1), [1,seq_len])))\n",
    "      prep_data_hat = np.concatenate((prep_data_hat, \n",
    "                                      np.reshape(np.mean(generated_data[i,:,:],1), [1,seq_len])))\n",
    "    \n",
    "  # Visualization parameter        \n",
    "  colors = [\"red\" for i in range(anal_sample_no)] + [\"blue\" for i in range(anal_sample_no)]    \n",
    "    \n",
    "  if analysis == 'pca':\n",
    "    # PCA Analysis\n",
    "    pca = PCA(n_components = 2)\n",
    "    pca.fit(prep_data)\n",
    "    pca_results = pca.transform(prep_data)\n",
    "    pca_hat_results = pca.transform(prep_data_hat)\n",
    "    \n",
    "    # Plotting\n",
    "    f, ax = plt.subplots(1)    \n",
    "    plt.scatter(pca_results[:,0], pca_results[:,1],\n",
    "                c = colors[:anal_sample_no], alpha = 0.2, label = \"Original\")\n",
    "    plt.scatter(pca_hat_results[:,0], pca_hat_results[:,1], \n",
    "                c = colors[anal_sample_no:], alpha = 0.2, label = \"Synthetic\")\n",
    "  \n",
    "    ax.legend()  \n",
    "    plt.title('PCA plot')\n",
    "    plt.xlabel('x-pca')\n",
    "    plt.ylabel('y_pca')\n",
    "    plt.show()\n",
    "    \n",
    "  elif analysis == 'tsne':\n",
    "    \n",
    "    # Do t-SNE Analysis together       \n",
    "    prep_data_final = np.concatenate((prep_data, prep_data_hat), axis = 0)\n",
    "    \n",
    "    # TSNE anlaysis\n",
    "    tsne = TSNE(n_components = 2, verbose = 1, perplexity = 40, n_iter = 300)\n",
    "    tsne_results = tsne.fit_transform(prep_data_final)\n",
    "      \n",
    "    # Plotting\n",
    "    f, ax = plt.subplots(1)\n",
    "      \n",
    "    plt.scatter(tsne_results[:anal_sample_no,0], tsne_results[:anal_sample_no,1], \n",
    "                c = colors[:anal_sample_no], alpha = 0.2, label = \"Original\")\n",
    "    plt.scatter(tsne_results[anal_sample_no:,0], tsne_results[anal_sample_no:,1], \n",
    "                c = colors[anal_sample_no:], alpha = 0.2, label = \"Synthetic\")\n",
    "  \n",
    "    ax.legend()\n",
    "      \n",
    "    plt.title('t-SNE plot')\n",
    "    plt.xlabel('x-tsne')\n",
    "    plt.ylabel('y_tsne')\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 121 nearest neighbors...\n",
      "[t-SNE] Indexed 2000 samples in 0.000s...\n",
      "[t-SNE] Computed neighbors for 2000 samples in 0.075s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 2000\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 2000\n",
      "[t-SNE] Mean sigma: 0.020670\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 54.858269\n",
      "[t-SNE] KL divergence after 300 iterations: 0.693459\n"
     ]
    },
    {
     "data": {
      "image/png": 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a4m8oLua2SITjkjGkpfH/kRFui0R47WCQdEuin6NRhuHu2gVceaVJbquvn5FXMvuRSDDE7MEH+VIlnjccpne+upq2PYAvu7iY++TmUpKwuPMFwWIOFiYNQ0MsBBcOG/t9YyOJ+vPPc21v20aCKklcoRD3S0vj9nCYDMbtJtG1jQVbO51kDJmZZAJDQ0bbSEsjkV+8mNtaWmiPt9uBhgbg8svJhAYH6Rx2OhmI1thIk9ZTT5FhFBSQ2WhN5jM6Stoi2oPY9UdGOFaJgAoExmsWkjLT308NqbUVeOIJ4L3vZV8Gj2fyzUxzFoEA8Ic/cPIcO8YHnpZGe15xMWOT16+nVz4cBu6/n5y6rIxJIldeSXuf5ZQ5b1jMwcKkIJEAdu4k0SwqIoH1+YB/+Rf6AiIR+hNiMRJVkbLFApBs6gGM83doyEjvOTksU11cDFxxBYXFRIKJYMuXk+A6HKQd52pZWLUKuO02M65AgOPt7KTje2SENGnLFmokQ0NGKBVtQqKbBPG4GbPDwUinjg6Wubj/fj6fTZuYBZ2fz2tKyG5+vlWC+w0kEgxZe+EFPhSx6XV1cXKJmpaRwQiG117jBBodpRNr+3bgj3+kbe+uuxhmdhFJrfMN1pOaYnz961/HvffeC7vdDpvNhh/+8IfYuHHjeZ3j4YcfxqJFi9DY2AgAuOqqq/Dtb38b69efEn02IVpaWvDyyy/jPe95DwBg+/bt+PnPf47/+I//OL+bOQOGh6kFxOM0pzz2GP0J4TCFvOxsk2OYSJioH/EXpKfz+Nxcfg8EKNkXFdEcs349GUJpKaX8yYbQjKwsfhYtAq66yvze2UkN49Ah0qmhIUYxdXQYZ7VEP4vWJHkSwaCJjsrO5v1+85vA979P5lRVRcuI200GuG7dPHU2J2NggGrogw8y7VxqqIhqFwrxBWRl8eHv3WuiGHp6TM3zQIBMwu8Hrr8eeN/7pmYCzUFYzGEMsRg11L4+zp3KSmPGvFC88soreOyxx7Bz506kp6fD6/VO2MvhbHj44Ydxyy23vMEczhctLS24995732AO69evP2fGcj7o7mYe4S9/aQrjSRKYZDkDfNYSfSQmZCkHcd11JJiNjWQK2dmTPswLQnk58Bd/Yf5PJMgofv1r4E9/Iq0SBiHO7fx8mqqGhnifpaX83tXFOdbeTm1KazKHyy+nRrFzJ5v4zFtTeVsbvfsHDvCByQMcGaHGIHZHl4sPr72dxN/h4MNNLnsj6pvbzfPt3s2HbOGssMpnYMoqdqOrqwuFhYVv1EAqLCzEoUOHcOutt76xz5NPPonbbrsNAODxePD5z38eq1atwqZNm9DT04OXX34Zjz76KP7u7/4Oq1evxrGxQj3/+7//iw0bNmDRokV44YUXADB57u/+7u9wySWXYOXKlfjhD38IAPjsZz+LF154AatXr8Z3vvMdPPfcc7jlllsAAIFAAB/4wAewYsUKrFy58o3KrueLjAw+v//+b/PcbDYSyZERfsTsG4uZ9ZqVxYCTT36Smcr//M+0za9dmzqMYSLYbGRgX/0q8OqrZBDveQ9N3QUF/BQX839xtLvdpGFpaXweAwOmT8O+fcAPfgB89rMUmGdhUvuFIRSiahWLUbUUk9DwMB9eWRmZQF8fH57PZz4FBcwC7OykBDI8zAnndBqbpcNhIh4AMpKT66hZmBCW5oDxFbsFkQil4MJC4xQ9X9xwww34yle+gkWLFuG6667DHXfcgauvvhof+9jH0NfXh6KiIvz0pz/FBz/4QQBAMBjEpk2b8PWvfx2f+cxn8KMf/Qhf+MIX8La3vQ233HLLuE5xsVgMW7duxR/+8Ad8+ctfxlNPPYUf//jHyMnJwbZt2xCJRHDZZZfhhhtuwDe+8Q18+9vfxmOPPQYAeO655944z1e/+lXk5OS8Ub9p8KRKdmLWHR0FDh/mWpyojERLC4l7KGR8CsnmI8lZqKriucR88md/xtyA2Qy7Hdi8mR8xiT/7LLOYOzvpDJfIKvGH+P18JvJsBB0dwBe/yLk41gZkbiISoTTR00OzUWcnJ0dxMSeIhLKVl1NSGx42Xv/sbD703l5KFzk53Hf3bhPJIPvn5FDi6+7mb1YkwDljRpmDUuonAG4B0Ku1Xj627UsAPgygb2y3f9Ba/2EqxzFVFbs9Hg927NiBF154Ac8++yzuuOMOfOMb38D73vc+/PKXv8QHPvABvPLKK28U5ktLS3tDol+3bh2efPLJ05779ttvf2M/KdX9xBNPYM+ePW90lPP7/WhqajpjdvVTTz2F++67743/85JSeSVbOBSiVv/UU2SWN91EE5AgFKINvafHbJOIJBHgJHN540YyhFWr5qbj1WYjA33ve4F3v5t0r60NePRRzjO7nYwhEjFO+eRjJUT2b/6G57nhhpm7lymD1iTkgQAfzt691A727qWTKRgkUxgYoCloZMQkqChF+1xmJrc3NlK7WLKEk/KllzgRh4fJCGw2HifFsy6y0vJ8wkw/pZ8B+C6An5+0/Tta62lrdux2n1r6eRIqdgNgKe2rrroKV111FVasWIH/+Z//wQ9/+EO89a1vhcvlwrve9S44xi7idDqhxsJskktwTwQxVSXvp7XGf/7nf57SvyFZUzgfjI6aZC/JyO3tpQnlfe/jtlgM+MIXGG0otnYheMIU7HYKhF/+MnDjjXOTKUyEtDSG1y5eTIf6734HPPQQrSYZGWSq4oKSJj7y3e8HPvxh4OtfJ6M5GaOjfP6zks4FAiT8Lhe7OEkTirY2pswfO0aNIBAwtknRGGIxEv5AgAxiaIiMwe2m1ALQkZOXx3MFAlR1HQ7+PXyYzMcKbT0rZroT3PMAprbL2zlgiip24/Dhw2hqanrj/927d6Ompgbl5eUoLy/H1772tTdaiJ4JWVlZ51RZ9cYbb8QPfvADRMfqTB85cgTBYPCMx19//fX43ve+98b/yWYlEdTEXOt0ch23tRlm+p//yfDMaHS8+U3MSkoxzPTxx4G3vnX+MIaTkZdHh/b99zNKaaLCfcnQmjTwC19gjoRgeJg+jmeeAZ5+mqbP8ykrnhKQuiU+n5lgnZ3UBIaHaQpKTyeHHB01EzEjgxJKfj4JfTDI3/fvB+69l2Gr8Th9EStX0sN/7bW095WUMBciHmeMsoWzIlUd0p9QSu1RSv1EKXXakmVKqbuVUtuVUtv7+vpOt9tZIRW7IxH6vbzeSanYjUAg8Ebp7ZUrV+LAgQP40pe+BAD48z//c1RVVZ1TpdM777wT//zP/4w1a9a84ZCeCH/5l3+JxsZGrF27FsuXL8dHPvIRxGIxrFy5Ena7HatWrcJ3vvOdccd84QtfwODgIJYvX45Vq1bh2WeffeO3MxXsjcdpH/+v/zKZyBJyLiHpWVm0m2/dyrLXFvhcrrmGEZrf+x7NdMD4Z601aWNODunot79NITgS4bOMRGh9yc/nO9izZ2bu5YKRmUliL47iQMBMIqV484sWkchXV5Owu1y88aEhLlKbjZMtEDDH7dhB2+fevTxHLEauLEkpo6Nc7P39M3brswkzXrJbKVUL4LEkn0MJAC8ADeCrAMq01h8823kmo2T3dFbs/sQnPoE1a9bgQx/60NRe6CIQClFDSEsDjh8/iEOHlsLv5zr70IeAb30L+Ld/M47naJQfu53r9u/+Dvj0p+dxSOY5oLcXuPVWEn1xTKelkZ4VFZEeLlxIM96VV1JIPrmxT28vc0DO1m8ipXDiBIn5tm3kcP39JNxiBlq/Hnj4YW5LJMgQRkaoLdTXm3R6u50MpKmJv7ndfGg5OZyopaX8GwhQAly6lDbOyy6b6SeQMjhdye6U0xy01j1a67jWOgHgRwA2TNe1pWL3VDOGdevWYc+ePXjvRMbkFEJ6uumgKutTa+BNb+KzevZZ7iOlbtLS+L/Nxjj96WYMiQQtEUND4yuypjKKi1li4/vfpxaRmUn/amGhYbTBIP2s3d0Tm6IkCW9WoaqKWYZvfrPREnJyyAw2byYxr6jgNqlTEouxZvvq1aaERl6eacTh8VCikSiK0VETJltby/jotDTTe9XCGZFy7iylVJnWeqz4Mm4DsG8mxzMVOLmvdKrCZuPaDIcpqC1fTlNbZSX9DmI6lvLXyRVQv/zl6WUMPh8zs8U64XAAy5YZH0duromgisf593zKbEw17r6bGsGnPmUilsJhjrG3lz7W73+fAu+aNeY4YYJn6DqbusjNNZl/L7/MGOBQiNFGBw6Q8Hs8jFwqLmYhLK2pdYgkMDJCaUAyoyXHwe83VVvFTKUUowPEAW7hjJjpUNZfA7gKQKFSqh3AFwFcpZRaDZqVWgB85GKuobV+IwLIwvmDZfM1MjKolQsiETKIgYHxfRbS04F3vpNrcLoQjTKJ0eXi9Y8fp5XhvvsY6bhwoRlbeztNZaLtSAvQykojlft8NFufOEHmuGIFhdipZna33cbn+F//ReG3t5eaRG4uLScSTlxWxm2xGPdbunRGGxdePBwOMonOTjpQXnuNZiafzzihJYW+s5P7l5WZHIlks5PWfGiixi5aBLz97dzH5Zq/EREXgBllDlrrP5tg848n6/wulwv9/f0oKCiwGMQFQmuN/v5+uJLq0SQSjD7y+YxZKRo1msSNN5JoBYPGfj6Vj39ggNfPzSVRHx7mtaW0f1sbtZveXtKXri6Gk+bn04bf1ARs2ECpvKeHdaG8XgqtHR2MrNy8mWbwqZ5Gt99OZvb//X+mCm0gQK1IEube8Q5aYVwuU39q1sNmI8F/9lk+gBUrTJkCSZA7dozEv7ubNx6P86W3tfGlS5EuKdoVClEamFXOmNRBypmVJhOVlZVob2/HxUQyWSCTraysfON/kayzsijESV8DKWPT1GRMNlqbbOipkm4lZDYQMHkZgAks6OigADo8zL/t7aQ1wlB8Ppptqqt5XyMj/C6MoL+fJbzr6qanIN7q1fTZ7NjBcfX3kw5Go9QefvpTRjvNuUZog4NU1yoqyJ1zc8n57XZqCKGQKbPh93MCSnMPt9tMOKeTnNNmY710CxeEOc0cnE4nFixYMNPDmHOQfJCMDJqFRZt3OGj+HRzkmhXiOjjIoJSiIhK3qqrJJbLZ2by+JJRJjoXNRhoxOmpoR1+fKe0dDhth0+8n05BotWQNIS2NmsjQ0PRVS73iCibNdXWZUH9JfNu5kxnXd9wxPWOZNkhLPqVMsw6ZNIkEfQ8+H30Gg4N8OBLNJC9c1C23m9xUYoUtnDfmNHOwMDXIzKSz99VXuSalvaeYiDdtMsRVEgqfeso4r5ViL4O3v31yTMCZmTQt79lDAp6RQYZVX0/CL70h7Hbja5Dif/E4BVDpJ+FwnJrfIe1Fp7PS87p1tJxs32461AEcv8/HFqtzjjlkZ9PuNzho+r9KunhFBW9eujxJeNbQkGkRGIuNLwOcm8v4XwsXhJQLZbWQ+sjOZjjrJZeQ2EuHtfR04M//nNoEwLXd0QE8+SStAllZJNgVFSy3cYFVPSZEXR2Tyy65hER+82bSi+PHGegiXekyMjjmnp7xobr5+WQwlZWkL9L2c3SUjKa0lHb+6YJSwAc/aBiY0D5pZ7prl8n/mjNwuRjeGo/zJk+c4EvIzjZRSNJ7Nj+fL7O8nJMqLc04oUUCqa3lpLuAMvkWUiAJbrIwURKchalDIkFz7jPP0E5fWkpnakYGJfjiYvoPt25llKKs1epqSvpeL/2Pf/3Xk1vmRmva6NvbaQpKT+dYMjLIEA4fZs03Se7zeCis3n477yESodlm1y6eJy2NjuhNm2amHE9p6fiChgKbjbkPc7I1gTiBDh40kUivvUapoqzMxCyL4zktjWGuyWFoy5dT/ZIKruXlzJFYvnyWh3ZNPk6XBGcxBwuTikSCzOHQIRLZlhauc2l/6fEwEMXnozn5r/7KaBrTOcbBQdNbobT01AJ2Uol2us1JJ2PVqtOXx/iXf2H11jmNUIgv6tVXyRT6+jipcnLI1aU+U0uL8Tvk5QE1NaZzF8B93W7+veUWMxktRnFa5mD5HCxMKmw2ErThYRK1detoHhZfwOAgTcYAhbmZkMZtNtOQ53Rwu1MjGuhM/tTOzukbx5QgGqWqJiahiSAv4uabaR46fpwRDT09JP7yu9vNc/T1UVtIT+f3ggJqDHKdJ57gsdIJcelSMhILp8BiDhYmHVJKIzOTloANG+iQ7u2lNN7dTQZy6aVWTtLZcKYQfYkam3XQmpJ+U5OJTqqtBRoaTt9Z6+hRfnJyqA1IJrXTyUk2MkLmMTBg/BXiMOrspB/D6+XfPXvonMrLY6JLdvb0q6+zABZzsDAlKCkxTtSqKmZN79vH9fnWt7JXtFXF4Ow4UxfCWduSQMpjFBXRH5BIkPCnp5++fO/evdQOcnL4f2WlyYUQP4L4GwD6Jo4fN13mVq0yfRzS03m99evJaLq6LOYwASzmYGFKUFnJOkwdHSY8dNUqMoQbbphZO/5swulcgkpNb/TURSESYYRATw9ti729pqAeYOx8zc008Zychp5ImOJdAolY2rmTkn8wSAaiFLfv20dfRSJBhuD3GzPT4sWUXHw+nmvWVS2cHljMwcKUwOEgE9i6lWvQbqeJZM0aizGcD3JzJ96enj5LIpVCIdZaESnf46FjedWq8U4dSbXv6mIYXDhMSWLBAt5sSQkdWT4fjwsEaDqqr6eWIIX3WlvpzHK5WIXV66WpKRw2CSvhMMfQ1MT9rr+e26yJOQ4Wc7AwZcjNZSMun8+U0bD6u58fgkHSzeSOsdKyde3amRvXOSEWY3e2AwfoWe/p4aeoyDSmELtZTw9NPbt2cd/aWhL/3l4mrSxaRIYwNAS8/rqpxtrQQJOQy0WtoLubZiQptFddzYm4ZAkn4f79TNeXgl+SNr9jB51gVg22N2AxBwtTCrt9Fpk/Ugxa0zpis/GT3GNa6OGZ0N5OmtfVRTq8erWpUDst6OoigS8tNYOV+kiJBH/PyWEI2/793FZRQQn/wAFmLYbD3K+6mszhgQd4ruxs0ynu+HE+oP37aZpKS+PDk+5JmzeT4TQ10cSUnU3uarNRk+nu5jj8/tOravMQFnO4QEQi/LjdljRsYWowNESBWqpIAKa2nDRBOx327wd++1taYqQL53PPATfdxGCAaQnv7+4mFwsGDXNwu5lduGgRa7BIY54lSyjxp6WRSfT30xxVU8ObaGtjBmNrq2k0ImnseXkk/F4vzU8ZGTQlNTdTA6ms5P7Hj5NLHjlCP0NlJTWQjg4yj2T1zILFHM4X8TgrYv7+9/y+cCHr8K9ebWmkFiYX3d2km8n+Uq1NNerTIRQCXnjBNF+SqLDBQVpkysvHNwyadMTjJOBOJ4muz0dJSmogDQ2ROdTVcX9JgunqIhd7/XUS9kCAmdEej/FX+P1cdIcP80Z37eKx6elcgGJeys83lRS3b+e29naG0I6O8tx9fcDKlTy2u3sWh39NDWa62c9PANwCoDeph3Q+gN8AqAWb/bxbaz04U2M8Gd/8JoueJRKck0ePskzEV75i5roFC5OF0wXSDJ5hRUg/C8nwFqFFfBft7eO75E0ahodJtKXxTjhMn0FpKX8bHCRRXrOGyWeC7GxK+mVlwPPPM70eICOIRCjZezym3Z3UNUkkuOh6ekjws7LIkLq7ebOFhTx+8WJqDTIG6b8aidD/sGEDa6xYzc7HYaYL7/0MwE0nbfssgKe11g0Anh77PyWwfTvwta9R++3ro6DT3s4SMI8/PtOjszDXkOyvPRmhEOkdQGG8tZW9cV56yVhnIpHx2mw8btocTHrVnHCYUn4gQMbQ2soB9vRwcRw5QuJ93XXsBpV8YwsW8PisLBJ8YSzxuHGuxOMk+DYbF11vr1mEWVm80ZERcsOyMv6VkhrBICW45mZyx1iMx544YXrgOhxG1ervNxmb8xgzyhy01s8DGDhp89sB/M/Y9/8BcOt0julM+PM/N33Lw2HOuYEBzqODB2d6dBbmGvLzz+zP+vCHTcLv/v2G3ra3m/4afj+3Cb2tqCDTmXSfQ2enacKzdy+5k7Tc83hoWgoGKaE7naYPqs9H4r5pE4+Jxfh/djZvrrubkn4wyOPEyWyzmUJ80rNFHNHCPMQpLVUXMzKoTYhHPzeXH2n6cewYNZetW5k/8dxz87pZUCr6HEq01l1j37sBlJxuR6XU3QDuBoDq6uopHdT991P4Mdc2RSEHB2nHtWBhsrFyJWvOTYQXXgD+z/9hy4LknlZlZaTTpaU89vBh0ts1a/jbsmWTOEBpNtHRwQsND5Mh9PTQl1BRYUpu9/SQ6Pr93F+863l5HNyVV/IcHR2mwY+k2StlajGJTyMnh8e63WRAS5fyt4ICcsDOTjKfpiZyw0DAdHqS8K+REUZFFRYCf/oTHdhS9jsQYN2Xm2/mg5tnSEXm8Aa01lopdVoFWGt9D4B7AFZlncqxfPGL4/+XsMJ4nN/f8Y6pvLqF+Yr/+39ZeuRk2Gykia+9Rjr24Q+PNyFlZjLP7M1vppUk2Vd7cgXaC0IgwNrn+/bRRNPcbNpzJjfe8fkoPXk8/Bw4QCKfHFM7MECmsHIlOVpJCQm6lNYAuC0nh+qUw0EtISuL1wsE+L2w0GROA2Qk0h/Wbuc+0kgoK4vn27yZEVFHj7K2fHc3NYhEgkzD7Wb47GWXUespLJw3yXKpyBx6lFJlWusupVQZgN6ZHtBDDxkf2USQHB0LFiYb73gHSwcdOGBq1NntFG7z8khLjx0jfU3OJ9Ga+2Rl8TOpiEZpejl0iExAzD9SPru01Nj2PR4OLh4nEzl0iFpCIEDi63RygIcOkfDabGwR+NBDlNZ7e0nkXS6q55WV3F5SQobU2ckbLy3ltaRR+MgI9x0dpS04J4dcsq6OvoZQyCTJHThAraG7m9qO3c6x7N1LDSQ7m+dZtozjW79++vrFziBSkTk8CuD9AL4x9veRmR0O8Ld/e/rfnE7gZz+btqFYmIf4xS/YjKi93fhN09MZhCNCbE+PqR03OGjM6VMCKUkRi5Fo+nxGJQkEyDw8HtPbeXSUpiKtqTFkZjJKKBKhKejAARJut5sSfFUV07+l5Wd/v0mQa2igJPbii2QAWVncT6R6cVzX1Zkw2l276NPYvZvXjUa5n8fDmPT8fNM0KBjk98xMPsimJpq7fD5us9kYanvllWeuijgHMNOhrL8GcBWAQqVUO4AvgkzhfqXUhwC0Anj3zI0QePZZChqnw3vfS+HDwsVDrBFpaadG2cg6DAZJJzye8T2p53Lp79WrScM+8AEKy8XF9DHk5vL+ly8nzRwY4LOoqGDJoSmjXWL3B8go8vJIqIeH+WJqa8m1env5v5iIamrIHHp6OPh9+5jcJjav1lYS4b17yRyGhih95eTwuJER3rzDQQIfCPB7ZqYJdV22jA/Fbufx8TjVr4oKfux2ahlOJ30fO3aQ69psnFz9/dQqOjt53pwcnuPECUY/NTbyvIEANYo5jBllDlrrPzvNT9dO60DOgB/+8PSJk2438B//Mb3jmY2QSJm2NvojDx0ifcnJ4VrPzeU6HRkhkc/OZrc4yVsKBkk/enu5ZltbeQ5p9VlZSSm6ooLWhpUrp5g4zgCWLmXG82c/SzolvtSyMhY43LTJmOen/L6zs01za/EtFBfzJQaDJJ5KAVdfTWI/OkrzTF0diXIkQg2htZXH2WwmCcPrpXnnlVfIdIqLefNa8/fDh3mN/ftZCyk7m0RcOGNZGXD55TRhJRK8rpT5bmujNiH/p6Xx//5+MpusLE7G5maOUfpBHDjAazz7LE1bodD4SVxbOycZRSqalVIGTU3UQsUvJmUMJEb8Xe86czOW+QatSReammiS3r6dwpZ0ZOzv51obGeH6loZALhc1e4kqzM3lOZTiOi8sBB55hO8iHCZd8PtJU+x2CqAvvcRzFRTQ5LJuHTPXly07cze16UQgYAROyec6H5SXA/fcw77dBw/yXjduJGMEppEZ5uVRkj85hVsqpEYiJga3vp6Lp7mZE6CwkAS7tZVEtbiYL/XQIRL0vDwyAUlUA3izhw+TGRQXU8Jobwe2bDFO49paSg41Nad3tOTkjDcDOBymHLgU+ovHOYGGhkwTco+HE7SnB3j0UV5DmJLXS5PZ5s1zri6TxRzOgH//d0qnUk1YICXjv/OdmRtbqiEaBX7zG+C//5tWgWCQ2zMyTABLbi4ZiEQ2+v3G/JudTSGzvp77DA+btRyPM4w4HCZNGBoygiRgSvG43aa8xO7dJJ7Dw4zaqaqaoQcDjv+ZZ2jBEBPZ5s00W59vyRWXi5GVN988NWM9JyhF9ayoiMR+2za+rIYGEuVIxCSWvfACQ6q6ujj4khIykJER4Jpr+OL27uVEcDiM3dDhoDYgtUJ8Pn5qashYior4v83GYzs6OLYzvejVq/kShoc5ztFRHi9SybJlxlktju0FC8jR29tNfSapO2+38/hYjBLLZZdN+aOfTljM4TRoamKIc1qaKRMzOmr8WP/4j/MiYOG0iEQoNEUifA4PPQR8+9sU/mR9x+MmSzcWI1GXDo/iWI1G+WxHRnie4WEKj+Ew6c7oKM1Jg4OmT4zWJkxdCnwqRVpis1GoBWhFuOIK+g+V4nXy8qbfP/HaawyGkdbGWVm0UOTkTHGNo6mE3U573h13AG97G6V2UYsOHeLL3LqVoVQVFeTUfj8Ju9dLn0I0SmL/4osmh0HCrJInjyTBdXUZv0ZHB/dpbub5BwZYH76qipNG6ovk5pqMv/JyevaffpoqrdPJ0hmxGIl+Rgb3OXCA9xcOczx2O6+fk8O/R48ajcHlMsl5J6u/E3F+rTlhxdmewrCYw2nwyCOcy6GQMYlKImVjI/C+9830CKcfoRDXy/Aw1w/AZ9PbS9/L8LBZ34CpRjA6yv8laVDWhhwPjK//A3A9Swc5t5vnGhkxa1UYgkCSYcNhCqMlJaQpEj0ZjfL9ORyMRJyurpDBIPC973FMYkL3ekkrX3llFjOHZLjdJPK9vST+nZ1GfYtGjcrocpEYj4yQ0EoK99q1zEzOyqKppq2NL1kpvlQxMXk8xmlst5vJFAySQVxzDV/47t2mMqHLRY2hZCyXdtkyqqd+v3F2nzgBPPkktQqfj4ymsJBms9FRU+JbHNFdXbRl9vRwApeXc+L/678CV11lGFJBAcdYXEwneH8/zWMjI2RES5bwtxSFxRwmgN8P/OQnfJdC6BwOYyJ5z3vGN7Ga64hGqTW//jrXfksLBbR167guXn3VhLiLQCTPTaR8+V8ii0ZHua7dbj5Tj4frWbQBWb9uN6+Rn2/K8Q8OGmaQXMpayqeLA7y4mMJdbi7P4XBw+86dXMNTXWft6FHgb/6G/hAxgxcU0MzV1zcF9Y2mG8EgCWggYEJVARJZr5fSeChkkuDS0vjQHQ5qHWIPXLjQ1CeXly7HSPhafj6vNzzMCSUOpuxsvtTVq0no//QnU/gP4H6RCOs5iQbhdNIsJZOnpoZmsrQ0ju3oUZ7rkUc4Od1u42zPyyNzkEQ5KZFgs3FsubkcV38/pci6Oj6jffs46SV7Oxw2Rf+Kiqb7zZ0TLOYwAb75TRLA5BDKaJTztKYG+LPTxVjNUezdy3l88CDnt9drTENaU2AsLKTAKGZYyRwXAS8jg+s4J8cIfAUFFOgWLDBFNiVEvbSU9ECqHtx6K60PAM8dCPC7HONyGTPw6CjpSmMjhcLGRqORuFykQ0NDU6c9DAzQcSzalM1mBNlEgv6TsrJZrjV4vWQCkodw5AhfnJTV9vnMSx0a4raGBn4vKeG+xcWUNi65hC/41VcZxdDRQYIaDPKF5uUZx/exY0brKCnhxAsGOVn6+6kmZmeblxsIcGzr1pHIv/IKTVEeDxlKdTUnlNfLiaIUGcrhwxyjzWa0B7ud2yTL2+MZ335UfCPt7ab4Wn4+JZMtW3j/LhcJic9HZvjcc8Bb3nJhEQpTDIs5JMHn43v/wQ/4bsVWLt0Ew2HgzjvnR2ezgQH6Ep98koXdgkFD4KVDYzRqQsFra0kIR0cpmEWjZAJOJ9fNhg3ARz/K9ReNkui73VzjSvF/8QWMjHB7snamNYNVfv1rajBSzqeri1rGpZeaOm4DA1xvDgc1nKnOQ5Gw+O5ualgPPMB5NDJiTJHioNfadGa74oqpHdekQDKdpV8CwIe/dy+Jo8vFG6+oIHHevp0vwenkftEoH4DDQU5dU8M45aIinjcvj8ceO2YqBlZUUJtobSVh9/l4nawsShLivGlspKQuUU6Dg9QSkkMIPR7+fuwYCXE4zAX82mvAz3/Oc6xfz2vk5ppIKZfLJMNdcw3H/9RTxskm5Tdk0ksZj54eHiNlO7ZsIXMQG/XoKCdJKMRndPQopZ5Nm1IuHNZiDmPw+ylUHD1qomGkVIGYSVwu4N0zmpI3PThwgKXJX3iB603ynfLyKPEWFPC59PdzvZeVcb3ccAOPPXiQAldNDQngm9/MSs3n6gieKApRKVoiPvlJXuPAATKBEycoPOblcQ329/P4u+7iOnzppfE+jnCYdOVs6zD5mJMRDlP4HB5m8u3LL5MmSMLuiRNGoAB4PTGbixb17neToaYs4nGTixAOk5CtWUNp/8QJmozEVpeWRuLa1kYntNNpJObiYk4ipZivsGABX9bAAI9dt47Ee+dOctCqKu6/fTsJ+9VX80X6/WYh2u0cS0EBF6vbzfNKREIsZlTFWIwv5o9/NIlt27aZarC7dpHg5+Zye3ImZlER7yUz0yTqxeMmGUfUwrQ0MgabjdsjEY5Fyo1Lso4wNb/fRGZUVHDbgQNkECkEizmM4ehRzrvHHjPJRIBhEvE453lKL+hJQDQK/NM/UZgRP4JkIvt8nMc5OYZx+nw01+bmcm0EAlzTFRUM13S7SdQnK0IoPZ10YdUqvpvDh+l/9Hq5psvKyIzEzLRsmXGeiyazfr2pCSd0p6eHNKqpiULx6CiJ+IoVtD6Ir0WionJzyQSPHCE9k8hIiYQUK0MsRtoiYbcOBwXR66+fnOcxZWhqoonm2DHepFKUgtev501I5JDLxZf98st8kGVlfECdnXzIIkEvWkSOnRyCKqFjL73Eh5eWZqSzeNxUb73xRj7AEyeY2BEOG1+Gy8UoJZEoamv5MgcHDfEeGeG5urpM+JvDYfwOmZmcuC+8wBfrdhtH+pIlpmjg9dfzPNI4SHIyQiFjN/T7OX5pciQhuJWV/PvKK8YUJdEtdXX8TUL3UgTznjmIL2HfPhKZPXvMb8lMQikmVc1laA3cdx/wxBNcK9IcRiDrta+PBNPppLDzjndw3f3mN1yLy5bxeR0/zt87OmglmMyEQZHqGxu57vx+rveCgvGMqLraJO8KPYrFGEra3s6oxq1bOcbhYXNuh4PjfeopUxJoxQoyGqeT/4dCNHt3d5MOuN2mRXJODschVhXxXV55JfAP/zBNPZwvFNEoX97TT1OyligD6TD0kY+QCPt8vKkTJ6gp+HyUjnt7+cnMJFeV/gzSjOfkaA6R9L1ek4QmZiy7nedau5YvZOVK7tPVxd+qq02WY1YWJ59w8ZISnrO9nS9BQlyHh3n+ri6OOzvb9KOorTUvPz2d5+rrY6ahEIQlS0y5D7EbDg8bh3xuLifJ8eOmvamYoJqbyTwAShXStnTNmpTrRDdvmUM8TuGotZXvSkxK8bgJwRTYbCQ6H/nIzI13OnDvvSxNPjAwvmaaaNWiReXmkiDX19NSkJ1NKbqhwZhVHQ4KcM88Q8tBODx12eTZ2Wc2E4kWIfjd7zhe6eAnIbgCydEQv1NaGmlMMMjtOTmcN1IRWhJqPR6Ow+02iX1SFDQ/H/jEJ/hJ+TpQwhy2buXEF2Le0kItoq2NBLKpiZOlo4OE+dZbacv3+UiY+/s5efLyqEa2t/O4k1FeTg4rdZpsNuM7KCzki4hG+WKysri9snLisefmUhKXPIlwmC+/p8f0T43HjX+ipsbkNHg8JNhCpEdHeY8LFhhboWgo7363KSGSlkatIxTi/lL3qaSE17jsMj6HV14h05AEu+ZmMjev16izKYR5yxyamkzQgtfLhdzczHctmp3YndPS6OBMbqgy19DZyegaEdykh4vWpraR9Em55BJqDrm5lNwlInFggL42KV8j0YfNzfRHpAI6OqgdlpZSW0wOhQXGh5eKU12kfGlNLMX+MjIM7ZHn5vdTyFSK88rtprP87rtnkUnS5SJzkJsPBHjTUp67q4tqVGMjtYHCQhK/sjKjMjkcnAC1tcYhK/kOJyd/SY5EVhYfshBJyRWQ1p3V1WeXMAYHOQ6/3zjFjh/n+bOzeW0JcUtP5/jb2njehgYzTpvNHF9QYBxtL75opAdhJkuX8v6rqkgkjhyhSU40m/x82j8BPsuaGlO99vhxmtyOHePYHA6eo6JixpnFvGQOsRjNA14vF3dPDz8Sfy9EUMIx8/OZ25BijH3SMDrKKCDR1EVTFogPsLSU2sKqVdTyKypMBF5nJyMbIxET+i59VzIzJ6nBzCSgo8PQO8muBk6tm5UMqeEkSXTBIOlHVRW1j4EB0hWfj8/jkktYKXXlStKGWZcTozVfXChEwul0GsLl843fNxSipBCLkSg6HCSWx4/zISxcyN96e/n/RFnBaWm0P+bkmNTxwkJOpqYmMpiVKznhzgaPh2pdWxuZjSSheb2c1MKoROqTJLY77uAxIyOcwLEYnU+iUra0cNLk5PCFi4Rw8CDHKtpPfj4XyaZN/D8z0zyz/HzDpKTa68AAz9vXx3tMJCi9DA+T+c4gUmTJTi9GRsjICwtNLH1bm2lTK10CAc6f2tqUzVOZFOzbR8YgBTYlTwngNrudDKGggNq8y0XhRvbx+ShgDQ2ZMtJ9fTxu40Y+v1RhrNJbZmCAWn9X18SVDCTBTsJt43HOF7uda1cCchYvNr6OhQvpUygrSznz8bkjFGKkUE4Ob3JoyPReFgkhHCaRA0xRLCmfIa1BpUdCIEBpvqGBRPN0sNvJPHJyKHVL7+dbbz2/ErviHI9GOU6RTIQpyIurqSHTWLWKJp0VK0x+w8KFZG45OTRRZWWZ4oE2GyVJ6bAkJRRuvtk4oqXhRnU1fQlpabynri4unP5+k+8gURI1NcZkUVJCBrdgwYxKFvOWOQgRBDgnFi1itJvk2tjtfC+lpfzMsYKLbyAc5rpOT+c9Dg7yvhMJCi+ZmRQMKyv53MSq8PLLzF1wuUy1gzVrGA0o/dslM7q4OHU6K5aXc+1JFGFbG03hEpGWDJeLazYnhzRQGop97nPcLhWi55TgcOgQufv69YzQ2LvXRAcVFgK33MIHuGIFpeoHHuAx0qZTHDSbN3Mheb18SMkZymdCVRVfUjjMc51v9E5GBifss89Scu/uNvkLEmss+RL19ZycopEsXMiXKQkzN9xAH4pkR0s0xsKFnBzS+2FkhDHbmzaRYYyM8Pn09HCCRSLGWS52yECA9yfJQ5KMEw7zI6WLLeZwKpRSLQCGAcQBxLTW6yfr3FKht7vbzL/+fhIzl8tk9gIkbkuXcr7ORcRinKe5ufSbPfssBSAJIKmq4pqPRo0WtWwZ5/uxY/wu2cpSXUAc+1qT8YTD40PPZxJFRZT209I4RgmoaWvjHEhP5z1edx33y8w0deMyMsgcUkULmnTEYiRohYXkfFdeSUbQ28sHduON/G1oiA/q6adpSqqp4aLx+/mAo1Ee7/GQkNbUmIWVnAByOtjtfPAXioULaY4aHaVUk5HBl9bXR8nG6+WEr67mBM/L42Rtb+dxnZ10xldX8757ezl2YXxSX0lqvthswMMPU/OJxUzl2USC53Q6eWx9vYlOklo8OTncF6Bd9sgRs6BiMTqsZqikcAos1zPiaq21d7JPKtEtMu/DYZMLI3Zo6RqoFHDTTalB2KYCbjc1hOPHuR6vvJJro6PDCIj9/dy3vJyMUrSojg4yh6IiU35bGEgkwvNu3Mi/7e2p4ZC12biGq6poUZAKsm4373fe9+dIJtylpTSzZGZS8k1LMw6W1lYSxvR0EnMh6JL7UFVlGquPjHCB9fbyBdTWkoBPle1NtAOtydW3bKEmIE7yFStIsCV9XylO0D17yPgkk1LyO6TiqoQyBgImM7qggPvs2mUKf4n6XVxs7I4Az71mDe8/GOQ18vJYNuS110zDInGKvfQSn/9VV9GRNc1axBwleWdGdjaFmePHTU7O7t0mBFNr43PIyiJBnKtoazNdF0XDdbloPZCWk3v3knDW1/N5SFkRmfN5eaZm0r59pjRGXR3pQlUVGUkqMAfAhOPOVVPhBUNs811dfKEtLXyRfX38+9prlB7q6liKQgirePOdTpNNLSakYJCZpeEwJ1NmJlXLUIgO2KmCmJdefZUTsKrKdFsaGiKT2r2btVhuvNE4oXp7Te6Dz0dNIi+Pv119NX9/7TUTFdXZSRW6spJJMZKu39FBLaO01NTbkRyQvDxeo6uL183IMBVqJfN6eJjHR6OmhtVll02r2prKzEEDeEIppQH8UGt9z2SefOlSMvbu7lO1huT+DYnE9JV3nm7EYpz7jY1kCC+/zHUjoanvehef0+rVDFEVxqA1fxemmZHB74ODfJ4ZGXy2knsgPjcL547eXgqukQhpsfg4phyLFpGgHz5MwpebS6JYUcEBBAKcIFLqWikyD4lECIU44IoK7vv733OSFRRwoWVk8PejR2n3n6hWymQgL4/Sn8tFgu33k2H5fLw/KbPhdFJr6Okx7U2V4v52O3/PyDD1lqqqmCgjoa4ACXxLi7nn1lZTUqG9/dRibF4vn68QmuZmnn/xYj7LI0d4TWmbKqWL8/Np6pqqZ3YSUpk5XK617lBKFQN4Uil1SGv9fPIOSqm7AdwNANXnWV1NKc6ZwkJW0BRNQZySEsGSKtLuVCAS4TrZvduU3ZY6aCIwlpVxfjY1UYCpreVcr642PrSMDApOEu7e18d9pCy+lOy3cG54/XXSVIDz8MUX6R9+85unWHCUkg6rVpFYZWcbZ4vA7+dkkNohQiR9Pk6aNWvY/AegRBEIcKJJtVVpCFRUxO2bNnFSTTbnq62ldiPNPyTTOj+fRDcSoVQv2k5RESeuFMJqbaXam5nJRbJ0KSV9v9/kcUjTIZ+P5xDJv6jI9NL2JlnFpbDX8eOmaCFAJilNkKTRkJQal8iwwUGeu62NDKqxccq1iJRlDlrrjrG/vUqphwBsAPD8SfvcA+AeAFi/fv0EEepnRyJBJn5yfLvEv2/YcCFnnR0Ih4H776fQE4mYcg9a05fg85FxiuZrs1GDXrqUdvtkrFzJSKXsbCNIlZbyfFKC28LZEQiwEm5+vvF/JBJkzEuXmi53k47OThKm5I5M6enjGUM0ysik11/nZMjJMZKB308vfmUl99m+3ZSmPXqUE2dkxBTHE4estAc9X6erlAaWmignq6YeD/CmNzFzWQpf5eaSAQaDJL5am74SFRXUHrQmoT52jPcvTXqamnjOwUES5tZWkyQnCYJyf1JDvqCA15KSA0rx2H37xqf0C6PYvp2MQCKVSku5PRzm8yot5TNva+O5xZE9RUhJ5qCUygRg01oPj32/AcBXpuJakYhpP3vqOGZJWeULxMMP0wwUiZjCmdIRsbmZVoAdO0zV1cJCzskXXzS9UQSZmXxWg4OUcgFT0HLWJYHNIHp6jFArkCi6Y8emiDmI0y0/ny9Na0q3Bw6QcNlsfPmDg6YjmzhsBgZo88rOJqE/coTnycignXJgwJSsDQbHRzK9/rrJS7jjjnMfb2srx6Y1z93VxfMsW0apRSTyRYtMXaRYjJVfd+6k8yw9nY51sR+XlpJw5+fTgS1lgPPzaVeVulL5+dSaJLdDCgY6ndxHSnaXldGsdP31JsS3qMjEdydnikuhwRUrqD20tvI5iVNdyoxLxUsp/TsfmQOAEgAPKT48B4B7tdZ/mooLOZ0kkBNBcn7mInw+02v9+HFjhpX2va+9xvkpSZy9vcaMlJnJbSc/G7vd1ECzcGGQNggnV5nQegoj5trbTb8FqSEzOmqk86EhEiibjc5bsbUDZArd3YZgLVtmpHiPhxNNOkNJWrpoDVLUat8+Shb5+WdPiBkeprnK46GkvXu36QHd1saIoz//c05kl4tmq2PHOMbLLjPlfNvaOIndbjIP6Ui3di3HL5M7WcKX6quVlVwQUkAvGqXDOiPDaAj19WQ+q1adek8VFSabWinTJWvlSkZ/CBMVR2hGBs/n85FBANPSRjAlmYPWuhnAqum6njT0EROeaNVSP2cuoq+PdODQIdOeEyBNkBpnUhxTEgYPHeIzWbDAcjBPFUpLyWD7+w2jlVpxU1ZNYWSERGnnTjKBQIAXzcmho0MyhF97jWp2RoYxzQD8LRDgfskTo76e2kYiYTrDSaXC0VHe1P79vOmtW0lElyw5s6Ovr4/H7dplQgxdLmPu6eig1HPppdw/M5NEVyKj4nFTTruvz8RfV1dzYifnZ0g0EUDmJuarri4+J6mxJA3OCwr4d8ECahtr1kzM7JYu5bFdXabR+YIFZHzSAEniv10ujk2aBEnRMlHPpxApyRymE9KOVvJOkre73SleWvki0NNjKoue3L8iFDKRfaIFi5+uqYnJr3M1gmumkZYG3H47E4+PHzdVYW++me9j0hGNUgJ+6ilKyYWFfOnS0Uga4UhlQamRFApRIm9pIVMoKuKkkAxggGGxhYUkkk8/zfMKp5Nw13icErZkmR44MHGccbKk3N1tpBiPh+eKRDipKyvpRNy8eWInt91Oc8yZTDILFvAaixaZuPbkMt/bt3M/savK2Gpq+P3uu6mBnC6Pw+mkCSke5/MrKjKqujBipWiWqqmh0y4S4fM7cYITYRpMGhZzGKu7Jfk5gkSC82euJr/5/aaMCHCqKVR6QUvfk1CIv7ndNKPO2SzhFEBJCVuqdnSQfkiviEmF1iTETz7JUtKvv26yhkUiysgwJqf2dk6C4WESxwULTARNWZkpulddzQnk83GCSavQ9HTuL7ZKqdFSUEANQwhperopeQGQKO7ZQ4IvaqtkOEsBLJH+hWFIi8/ziYCKxXiPJ05wcpeV8R4iEd7XqlWU8Nva+BwyMkjIpSCbZIi///2sUX+2BXL4sIme6uqiac3rNSXSpauW389teXm8N3HmT8MCnKOk7/xQU2MSPuNxQxRnKGt9WpCTwzWUXGRPBBYpWOlyUauSplj5+RTy5nJ4b6pAqj1PCfx+mpAeecTE9WdncwK0ttKkUl5uurK1tJicAGmXuXcvJevkUNe2NjprpbCWz0cC6HZz3wMHOIkAY5YJBmlSOnqUtn9xiANcjI89Zmo3ac3zS2MeMb1II3NpGXrFFbx+PG46rEnmcVbWqRK91mSOPT2c6D4fneROJ5nCokVGC9q2zXTCstt571KLpbiYKt7ZCLd0tXO7TdXL+nqeIxQiA8zJITPyePi7hPuOjPBvJDLlZo15zxy0ppAgiaEyj9xuU35+LmLRImqrEr0nFVlFO5CGPpmZ3L5kCdfKpZdeXNkbCzMMaWixZw8JTU+PqWAqNerb2zk5ystNX9REggykvp5/W1pI4CorzQQSZ7E0t/F4TMRDf79JKistNSUmBgeNA+zgQRLYv/ornrOjg+eThjxer+nPICF0kmMgfgPpNNfayjyHQMD4DaqrOd41awyTAniOnh5eu6mJmlQ8zucCkJkuX857v/VW4HvfM52gcnLIDEdHuUjE1BSPc7wSB56fz2fY08Pr7d1rutJJS8FYzLQSLS7msyorMyUJhInl55uGQUuW8P8pMHHMe+YglTWDQT5jmQ822/j8lbmG4mLg05/mGmptNevb4aBw1dBAH14gwDmam2uCOSzMUsRiJLb5+aastJTZlUqh0lS7uZlEuqKCfWP7+kjcOjupDUjFxo4OSuxdXSTc0hazt5eEUZhCZiYJ58gIz1VRYZoKdXVxLAMDHM+PfsSiXPK/3U5JWTKPi4pIqDs6TKLesmWmYOBDD3GsmZnUZrQms/N6ScxffZV9p5NLfNjtHOvhw9yenW2itAIB07WttZXPSDpgDQ/zfi69lOeWMhjPPsvrSEPxoiLTHOjAAdMMqKCADCMcNnHlUuzP4eD2oSE+i6NHeU81NWTOjz/O+1u4kAz5qqsmtSbMvGcOgGkKJe8okTBZ99KoaS7i2muB//ovlqDu6DClMaQ73s6dFJg8Hgo2TieFmsbGKTR5WJg6iDnE4SBxlhIZIyMmIS0WMxnAO3YY22NPD4mT02nKRHR2kjiWl/Nvbq6JuMnNJeESE4g4qYXoihlIHNOJBAlpcbEpC9zRQfNOVxcJspS3kAiKkRFuHxjgvYgfQqJ6GhtNhNSuXaafq5Q/2LCBUr9UjBVpMDniSurY5+ZyfPfdx3uQNoBKkVG0tfE5RCIsmPfcc4ZBBQJkCEuWkOj7/WRWr7/OBZeTw3664oTWmmO02ejf+eQnGeEUi/Ea0vBenu/gIPCHP5C5vO99k6baW8wBlJT7+kzZmESC72HfvrnNHGw2mmc/+lESfSkL09dnml0dPsw5LSZVET5zc8/ct9lCCiKZ6NXWkusLUZZM5dxcEpfaWmoC0jKxq8uUkjhxggRteJgEsqWFx9fX8/jOTqqewSAJfHExr+H3G/9ALEaiJiaXUMg0Bqqr4yRzOrlfc7OpeBqJkKD39fGYWIyf7m4yr6Iikyzm9XL/tjaOQyqoak1H/MAAGV1dHSd7RwefwcgIx+LxmOuKan38OI+JRPjx+fjb4CCP/+Uv+XyiURNRJMlCTU08Nj3d9Lnw+fiRWk/9/RxDWZnJ4g4G+fydTn7v6zO+loEBfnJyeP8HD05amKvFHECtz+/ndzG7xmKcL088wbyfuYycHK5xyY4W36PHQ8GrpYVrftEizluHw3Q7FCQSRuDLyjIaezzONbF3r4laXLp07uaPpDQyM0k8BwZIYKX/wJYt/F2cq5KS7XSSsA4OmnLFHo9pD5iVRYbgcHA/6YzV1macWSUlNKWInba0lN8dDlPgTiJBRPIPhfhbdTWZ1PCwiUqSDnMDA9x/aMiUwZDY38pKk7zX1UXim5Vloq5ycjimPXuoHicSDH2NxykRShRVdbWpRNnQwOcnvWLdbt6PfBcNoq8PeP55EvfsbP6WHC8umeRZWRxvaSm1gaYmLprMTC5G6TAVi3GsotVIIyBpMhSL8ZmIFtXSYjGHyYT4e07uBAawAOO//Mv0j2k6kZZGgWbXLpPJH4+bvuuiWUlkoASBiHA1MsI11t1tNP8NGxh88vTTDOHOyiJNaG6mdnLTTRaDmBGsXEkC2NtLIrVgAW3WTz5pJAS7nUzE6yWRj8VMCQhp/COmD2lck5PDCZCdbYrUXXEFmceRI9y/qooE9F3vImH/4x85ls5OEuO+PtMsKDlnYvlyXqOri/ewezcl7OxsSv2BgAmXra83Eo4406S5t0SYhEKUsgcGeK/CmBYs4FhEi3r1VW67805KRjYbbfuPPkoGJISjr4/MY3DQZIH39vK8FRUmLlxrMoLmZjIDn49aVThM5nT0qCl5PjREpnj11aaTXUUFn6U4rmVxihkpuSn6JMBiDuD7n4gxAMb/lFxHaK6hoID36HQaoUTm8q5dNCvV1XFbRwdNpUuXGj/FwADXxcgI16DdDjz4INeRlM8RTSIry+ROzXD/9PmJtDRGFYTDfHG7dxtH6muvkbgtXkwi+8orPCY72xA9SQoSxiIx91lZZhFJTH5dHX+XjN9EglL0okUmkUgaCg0M8By9vcwTiEQ4EZ1OTlBpOrJ4MSfTtm28riTsSA2l7GwyNZ+P55e+Cw0NvHcJzZUaSKI2S5HATZvIIKRdoNbje1i///1kUi+8YBqvu90k1M8/z8leWWmc+y0tb1xHL1qMwHEv4I0g0+aAzeHgIios5DORcYimtmoVF9GRI2SGtbV01I+O8t1IaKFIZHl5p5YHvwiclTkopRYB+AGAEq31cqXUSgBv01p/bdJGMcPYuNHk0JyMaJSCwjvfOf3jmi7U1FA4ETOqy2VK60SjNGNKMcnmZiZ3lpTwf/FTDg5ybUuEYU4Ok26TTUyA8XV2dlrMYUbhcpHQp6WRINXUkKBJYlYkQo0iPZ1Ey+02vV9jMRK8VatIkLq7Scjr6+kQLSjgOSV5KBrl/onE+OJzsRid3jk5hkgvXkxzzr59ptvU8DDHLDb4ggKTACeVYbu6OPZjxzimd77TlAVRihM6P59mJQmF1Zr75OXxHKIeS1tA0ZQCARP6mpsLfOQjJNYFBST+6ekm1C8cJqOoqTHPq7QUgexy7B6uw/DRbiAnE25bBGvW+pEz6qXTXWtTD7+pic++vp5MMJHgYmlrI7O85RY+09dfN5Fm1dWT3s/4XDSHHwH4OwA/BACt9R6l1L0A5gxzSE+nkHPw4PjtkhD2u9/NbeZQUEDB5ZFHKGAdOmRCwwEygIcf5tysr+f8bG01a7Oz06wlCXcPh81akWhEwFQjsJzZKYLCQpp/RkeZwNXRwQkwOkoiV1dnGm1LddWSEhKytDQykcJCEqbkYn39/dREpEBZJMLjpPpqVxcX1+bNvKacp6aGppaREe6zezcJdG0tCeCOHWQcYpMMhzmZVqwgka+qohkqHDbRFZ2d3HfVKuMUKy3leCUbW8xNPT1kYMmmguQcAq05JrudjEuc4SdOmIY8+flcVBs2AMPDiC9dju1t1VA7d6DYNQy4EghlFGC7bTHeVHEAzsSL1ICksdCb30yV/dgxEvvlyw3RHxzk/Vx+OZ+X+EAkL2MSM3fPhTlkaK23qvGp6LFJG0GK4M47gS9/md8l8U2SR/fsocBRWTlz45tK2Gwsxf+Tn3COiqCWjM5Ozt3qaoZXl5VxvXq9nJ+y5sQ/KGHhLheFq9pa0+Pd45m7EWCzEpI9DJA419RQWh0ZoVSblkZzi0TxSEmBnBwS9eZmSrFuNwlaYaGJanA4yBSysw0Bzs4m4c/KIjFMNoUcPkyTkGQjJxIk3idOUAvIzeU4pK6IJOWkpZnEsHicppjeXhL7YJCM4Mknue8ll5DIVlTw2ocO8doeD8cvjXZCIV4vufNaJMKxOJ2mZ4PEvA8Pm2MXLHhD0/FlVyOk3SheswjI0EBeLtx2B4YHHBiIZqFk7Vo+y5ER3seOHcZ019xMZiQmONGwbrmFz/boUR6Xn0+taxKboJ9LgQ6vUqoObNsJpdQ7AXRN2ghSBB//OJ+vhBc7HOb9j47SRCIJcnMR2dlkkB0dJkv85CoDJ05wzUkQi0RBSo8BaRPa0WFCgu12amSPP85n6PUyX8jq35zisNlIaDZtInGUJjYLF5o6K+3tlJx27DDVRUWylT4P4giuquICEyleOrQlQ2sSxZwchrL29lJizs/nhPN6eY1IhL9JYp7UfCoupsO9vZ377t9PAp2fT+Lq8XACZ2fzXkZHjRnpkkuA976XGsjgoOlctWbN+BpNdjuvVVNjwmidTjLAhQsp5Uuk0ugocMkliKe5eYrcXBOei7EArHCMz+jyy9mfW+rZZGfzuUs/6vZ2amPSbKWvj9LX1Vez2NmGDaac9yThXDSHj4Pd1pYopToAHAfw3kkdRQqgoIDVMH/9axNZp5Qxjbz8MpPG5nK9pdtvB777XUr6gFm7EtAhpW0kQKKlhcxTNIGXXuJ2ERjXrzfPLy2N+1RVkcnk53N9WUhxZGfTQQxQSj18mC9ZQi5bWvhiMzP5W1YWiZjY0B0OkxEtjWv27SPBa21lZrPYGCXE1m438dOyPRzm8YEApX/pMCflv5ctI3GtrORkfeklElKPx1SOjEZNMo+Yr/x+Y8rJyeGkD4fJXJIbBwkkCXBggMzA4TA21hUrWKq7oYEEZaz2TNZY0cq4PQ32ZcuAw4eRGB6AHkpDzsZ8ILGcKndGhunt4HBQUzh0yDxryZbetIl+ieLiiaW4ScJZmcNYb4XrkruzTclIZhiBAOf40qUkgBLPn51tepW3t89t5pCRAbznPVy7kvwpJesBrpP0dM7X1aspcElhPr+f5uO+Pgp1a9bwfNK33eFguGt7uwl9ffvbDV2xkOIIhQxBkrIZAwNcLPE4J8eJEyaPobeX2b3STnTBAlOjqaCAdn2/n1E/jY2cBAUF3O/QIdOhDTBJSJLJ7HbzmCNHKD3fcgsJppSuXbaMDG1ggAt21y4T6pqZaeKppYl8aakhzmL3l8quE+FNb6IqHI2aZuoS5ipZ2Ulwuxnxd/AgkJ6eBSxci8hQGPX1QNaqDC6IgwfN4sjJMZFUGRkcm+RKCFMTrWkKq7OeS7RSOoB3AKgF4BDfg9b6K1M2Kl73JgD/DsAO4L+11t+YyuudOMEXWF7O73l5JvJueJjztLWVBHAu4847gV/8gubdSGT8b9KbXSoTXHIJmWZ5OYXEYJDCXEGBMX3299MiUFjI83m9RuM+cYIa/KWXnr0BmIUZRiBgSnEfOEAC2N9P30EwSMJYWsr/xS+Rk2Myfl95hRJ1YaExfyxdaqTfjRupdYTDNAd5vZSii4tNeeyREZOEF4mQ6q5cSZNMcva3UsA111Dd7+kxRf7EyS0tTzs6OHFbWvhZvNg4tiWBbSLU1vL8R46YPhf19fycpkz4ggUmsEtrG0pKMkxPlIwMMrOVK3n8vn2mSVJxsQkLrq7mcx0Z4fimuGz3uZiVHgHgB7ADQOQs+04KlFJ2AN8DcD2AdgDblFKPaq0PTNU1g0E+70su4TwRs6Y4WJUC/v7vOS82bZqqUcw8iouB//gP4CtfYdi2mFRzc0nA3W7ev8Nhqhc//TQFwZoaCmqhkCn139pqalXJXA8GTSWFaJQ0YEp6I1uYPEgp7eZmvsyDB5lZ3dlJitfXR3XS6aTEftllZALLlnFySBTTyQ5TSX7LyjKhrS4XbbivvEJiLs7Aqira9gMB/n/llbzORG0Jy8tZF+ahhzgJg0GOs6LCdLIDONayMp7z0CES+6VLaWY6XT8Im42MpLbWxH6fQyLURD2MTnnGb3qTSRySblxOJzlLYaHxeWzYcNbrXSzOhTlUaq1vmvKRjMcGAEfHTFpQSt0H4O0Apow55OdT6l20iFLx4cN8B1rzfWjNufWOd5jaQnMVmzcDv/oVQ3h/+lMKcyUl/FtZaZ5PaSml/3CYx+3fz/m8bp3xF0qBSwmIkXkvZWJyc43VYKYgLZPT0kwVBKXIvM6nX8ycRk4OJYOBAXL/V16hFlFUZNqEHj5M6bm6mmp4ZeX46IZAgMQuOTVe8gKU4gILBHjOoiIS8t5eMqCrrjIVViXCSUJXT4eVKymxPPwwJZ1olJNRnOvScMjtNlVe6+pMdNXZIH10kzE8bIoZlpRQGjqfctq5ucC7382FJH00PB7TCS4jg/eVXHJ8inAuo35ZKbVCa713ykdjUAHgRNL/7QA2nryTUupuAHcDQPVFlgmtqOD7yMigH+rpp01eC2B8Pt3dwN/8DfCd70x6cEBKIT+fyaA33EAzk2jx4ttzu03wRHY2CamEwb/8MkPmc3JM0IvWFAozM7mm43FGDZaVTYsQ9AakXYFkd9ts/L+3l5pQUxPHvXIlTQG1taQbFRXzuG92KEQJXuoJ/elPfGASwyx1mKR2UnU1pXCJDJLklje9iftIufDRUTIYcXh7veNNOeL8lVDa7m5TEiM/nxL+2Rre5OQAt91mqklKprXWpr2oEF3JML7QJi59ffSzOJ2m17T0ez4fBpGRwXtbuvTCxjFJOJcRXw7gLqXUcdCspABorfXKKR3ZOUBrfQ8YSYX169df4Bsl0tMpMbe2cn7+/OecS2IOkYgdpRgI8cwzwFvfOnfbiArKyqidHzjAuS++wsJChl9L0mtPD/cvKuKa7+8HXnyRmoU4uNPTSWdGRw096Ow8czvfi4X0jGlp4VgPH+b6DYc5Dikd1NTE9y1C7pNPks6tX08hePVq4G1vm9tlVCbE0BB7EwwNkYtu28aXK60xpZubhKlFIrTbHzjAh7lypcmQHhkxjmS/n4trzRpTvVRqxSfD7+f+4nzNzTXduZK5tUy69PRTpersbKr8LS2U7D0eY2pKJHj9YNA4xurqzv5cEgnTeCgW45g6OsiMpEeGmKoGBylhSDjtLMG5kLY3T/koTkUHgGR9sXJs25TC5aIpcfFiEv5/+zdul+x7Kc43PExT5rp186OvQXY2BbdIhH647m5WqxWnvVRYcDhMs6/9+9n9UXJ5Bga4/iSHpK+PJrzFi1n6HuCxCxdSWpce9Cf73CSkNpHgmlOKtKCvjyH3HR0mtHbhQo5BOj42NTFXa+VK/vbHP5qcDJ/v1Lplra2mz3ZbG+fHLbdM00NPFTz+OAmg1jTNKEXpurvbvPy0NL7cvDz6GCIRQ3ClNpHDQanq2DGaWzwevvyhIRa4y801UrsU6ervp/kqJ4cvTtRX6UYnSV+dnUyiETtwXR05eXLy2oIFwJ//OduOer1mIkq3u7Q0TiSJXmppMaU1iopOlQL37CEDFGK/dSufyYYN9MdIxFZbGz+xGO3Wa9fy/mcBlD6LCjWWANeutY4opa4CsBLAz7XWvikblFIOAEcAXAsyhW0A3qO13n+6Y9avX6+3b98+aWPo76d5yesd36XP4eAcysgA7roL+OIXJ+2SswrDwySuv/89n4mUp5dw19ZWk3iakcF1NDzM3+x2o9lLzTJJNI3HDXEvKzOdF197jeta+r/n5pI5i8lZiltmZPBdFRRwvyNHjLP7T3/i+nW5GDjz1FPcLn6TRGJ8drzWHK8k1WoNfOhDpE3FxdPS431m0dvLblC1tXyhUspieJjc1O+n1CRmoro6vhghvNnZrHefkUE10uczOQAlJVTjVqww2cEAX3hbm2k5GgzyuzAM6TkRi/ElZGZSuli2jOeNRPjSCwqAD37QOLMFEj4XjZpGQX4/x7pyJf/u3GnMTuEwr7d+vdFUmpuBe+812aBj9ZPQ1GRC+vr7Tee5rCw6zlesMJVWU8jkoJTaobVef/L2cxnhgwDWK6XqQRPOIwDuBXDz5A7RQGsdU0p9AsDjYCjrT87EGKYCBQXAb3/LpMm2Nm4TISkU4uf732dQxeWXT+fIUgNZWdTUa2oo3DU1mXpjkYipd2a3m/wjqQYtPkrJoZDEWJHae3r4rP1+CpVpabzG4KApkOjz8bw33cTrNTUxUVR8jFJSXDQaoQXiN+nrM1YQYDxjAMx3sZocPsw58atfUdisr6ePdCpNYjOOgQFjR5OXKeWsGxqMeSgtjcRR+iysXMmXFY9zIkhimZiEolHmHrhcPK/Lxc/QEAnqZZdx/7Y2XisQIDNRiuaatjYuPGlpCvB6Lhc1E4DSyBNP8EWtXm3uo6BgfLkOsRULA5EJJxpBdjYnp4S9hsN0nqWlcaK63Rzj0BC3Pf44r9nXR4Y2PMww3d27qb1Ie9VZENFyLswhMUasbwfwn1rr/1RK7ZrqgWmt/wDgD1N9nTNh82YGOnzoQxQWRkdNXo7dzvl4113UKKcheCDlYLdz3q9eTXP03r0kuCKJS2i6FN+TwnzSCsBuN/5MKV+f3L5Xuizu2MHfJNrRZjMNug4fpsAaDJpwdukCabNxHQLGTyLRgW43hTqplyaC68lwOjn2QIDXKSw0fWK2bKG/9PLLWbsuhYTByYHTSZOKz0dilp7Om5RaRdIXurKSf7u6THlfKRR38KDpUVBVRRNQZSWZwMKF40shS8lugC9FfBk2m3H8jY4aKcLhMPWUAgE6t0ZG+PIlp+HoUTKMlStNDaZkJGcXi9RRVDR+n+xsjru2lkTf6+V9iQ9DSilI5Vefz4S+OZ38v7KSTK2sbNZMlHNRjKNKqT8D8BcAHhvbNm/iNlasYJ91CWuUzHaxT7e1sYLvfIZYFN7yFmrf117LjzT4kWxrYRDyAfg8JVx0rOTMG0zDbucaF1+FEH3AfJc2A3IdgdZch+npJqR9+XLjxxATYXY2LRmni0QSP0puLmlGfj6Z4MMPU5t55BHgr/+apUc6ptwrNs0oLqbELoX0xERTUcGHn5VF9Wn5cmoOjY3cf9Ei2t7Lykgwi4ooTZeXk1Amq23iF0gkaPPr7SVBDwRMRzfpTS015aVFqfROGBkx5iIp0dHZyf8l1FbqQJ0JMsmE+YgK2tZGjWjPHvZqfu45qqpNTRxzdzev19fH46Urm4TuCkOUcuizxCl9LizsAwA+CuDrWuvjSqkFAH4xtcNKLdx2G2sOybuPRMbnP/zpT8D99zM8eT5D6o+VlAAf+AAZ6rZt/E2yzLOzaQkQ60R2Np+nVHaWhEPRutPSuI8ElghzkPVbWMjjpOdMOGyiE0tLmfH93HNcl1rTsV5dbRiV3W40k/Z200dcHOuLFpGmeTymbeqWLcZ3cuAA72X/fvow/uZvgK99bbqf/BQhK4tqoRSwKy83lVC9Xj5Ev58PorqaatnQkMl6HhggU/B4TG9pqdxaVGReXDxOSfz4cTKa5maGlA0NmTIRYo4RFb2y0nDroiI6paTH9fAwpXzpqJaXx+/HjlFbOZ2a73BwX6ks29VFphMI8IXv22dyDqJRjju5RShAhuFwGNW4r4/P7dAhak6zqBzxuTCH67XWn5R/xhhE+EwHzDXk5gL/9/8CH/6wsVFLwqgIGt/6FnMCZoEpcVpQXg587GOUpoeH+ffBBxl0IuYesVT099OKIHRCa9OTXmqqBQIU4kTylwS6igoTuJKXx/WckcGgkBUrSMdWrOB2u930txGIwPrBD/Kc+/bR1DQ6ynNu3szx2WykF5KnlZNDDUI6ayrF8X7jG6RRn/vcDD34yYYQ4aEh3ugtt9CWt2sXX6Y4goaGyAyqq/mAAgH+VlzMCdDaypcn2dDvfS+PO3aMtvuODnr9S0vJcNrbeb3GRnL23l4+WOn5oBRfTCLB4woLea54nARYJIQTJ0zhvnCY17rxRjKP5ByJRIKcvqeHL/m550y/6eJiMoHyct672817k8UvamwsxklQXs6xu928d7udE3DTJtqg163jeFMc5xKttFNrvfakbbu01mumdGTnicmOVpoIb3sbtUphDPE454VUIX3Xu1h64nQlWeY7YjGuKSmv8corXN/Fxfx9cJBrWvwNVVWkTVpTA3n6aQqfDgcF0htvJAFftYq+PtFAzpYXlYxAgEl+ShltP5EwzY7uvpvnfeEFWhV6e+lztNtJJ2w2Yw4TuFwc7/LlF//MUgZamxCztDQSzv/9X0MUw2ES7/p67hOJkNO+9JI5JjOTL3XhQjrypDvczp1kCCJZ7dtH7hwK8eW2tlIiz8oit5a+CdKpzu1mNFRODp3k3d30B0iz8+pqjl2iJCS/QnpcB4PG3OVw8Ng//tGU26iupvbkdnO/wUGOU1qEDg+b6pSBAM+flcW/djullYoKOtola/qaa6asmur54ryjlcb8DO8BsEAp9WjST1kABiZ/iKmPH//YtJgFjO0a4Dz5/e85V7/73ZkbYyrD4TC+vupqrvvTQRzX4gt45zvH94iYKIz0Qor32Wxcq9KMLD3dhNqvW2csH7fcQn9KayutHhKmezJjAEgnP/c5lh+ZE+juJvE9fJj/S08HCUGVGGK3mwT9ne9klmhnp2kxGouZ8tgbN1KSlt7MWVkkuoDprCZhabGYCYH1+3kuKfcrvRoAShHt7cwqFq3g4EFyfJuNZiqA3yX78cEHSbQ3byZTOXHCmJBGRjhJ/X6Ox+cz5QAkKisa5TjF4Z6WZirOhkJkBJEImZ6UIj9+nMxlCvovTDbOZFZ6GWzqUwjgX5K2DwM4i2dnbqKoCPjv/6bQMzDAOSaaZXo6//72tyQqd91l1eW5GJwc0CHBAJMtbGVkkK7YbDSji1BYWkprhcBmo7WgvJylUz79aRM1mbxPIsGxv/aaCbaZ1fB6KfXs3csbjEZpGolESCilFtKGDVwEhw+T00oHuYULDUEOhcgIQqHxJX/Ly8lxh4ZIpOVF5OXROS2aSFaWWVROJwm3MIfGxvFdqC65hIxk3z6OSUJrHQ6eLxgkA1i40ORtSBq9UuNDaSVpLhAwneeamkzj9P5+jkFUz9FRHuf385lI5uzWrXx+0SgZZPIES0GcljlorVsBtALYfKYTKKVe0VqfcZ+5hKuvBv7xH4HPfIZzQrRUEaKiUeCHP2QZ6sWLZ3q0Fs4FK1eakFqJklq27PR+y4YGRivdcgtNTAJxkotjvaNjVvkfJ0ZTE23xUqiupYWSkUQPNTTw+65dNNVIBctYzISKpafz4YZClO49nvG177OyKPH/5jckypKHsGCBKeBVXEznkUBemMBuZ9hZQwNfhNPJ80ofabfbMAe/n1pNVhaPkyQbkT6kkKAUBZTid/E4xyZMr7+fWsHgIO+7oYFM5uhREgK5pqjAkmkdidBGunDh+JyLFMNkBNxegDI/u3HHHVwLv/yliX4TrTiR4Nz47GdZnyk5g99CasLtJjP3+00v7LP5LRwORqktWGA654lZXSlGcc6JQn09PSZKqKWFxD0cHu9AcrkoXXu9JPqjozTXHDxI6d/rJSEVImqz0ebW0kIJuq+P0r0koEhGslRy9PvpwJVKqUNDvOZETt1k9fLkGkmjoyYHIxDg/52dNP9IdMOePSYcNi2N230+vtSaGjKozk7eizRp6Okh85DkmYIC7lNaaqqr9vXxesXFZIQSoZXCGbSTUQDgogrezUYoReJfXW3K1AMmIGJ0lCbXj350bvednkuQ8NnCwvNzaH/3uzRLCe2SnvOXXTYL626FwySa4lwFTGndEyfGZwTG45zcPT0k/hkZtPULEW1s5P/RqEkzz842JbHz8mjyeeQRRiYUFXGbZFB3d5uEu7w8EuOXX6Y6lplJk8zZuK/HQ8eWhLJlZppMSqknPzrKRdzezhdZUmLqLIk5rKSEhL+ggIxHakZJDsX113PySEFCqSHjcPAYUSWla15tLa8hGZopitmRqpeCyMsDvvc94C/+wjgzBfL9t7/lvPz//r+ZGaOFqccNNwD/8A981xJEU1fH0OcUCUY5O2Ixmo4ki8/ppF2trIx/MzIoAQtDCAZNa9CeHhJcsc1Lg3HpGZ2VRa2gpISLIRIhwZRIIqlq6nCQoPb18cFt2cJrSZzzggUcT1bWxDXepXVpMMjzl5eTcEsl1CVLjAYjRHpkhGazjAxet6jI1J+XLleVlTx2ZIT3k5dHW+H69bzO8eOUDsvK6KNYsoSmg74+HivnkTpNXi8nSiiU8tLDuYSy/jWAX2qtB0/ze0qEtU5HKOtEeO45ZlCfOMH5llw1FOC8fughEhELcxOJBC0Hx46Z+m2zKpx53z4S1oICSv5SRle6ub3yCvDv/276LRw/bkxNNhsJo1Q7XLLEJK0UFJAoSnJIVhb/pqXR7NLfT4IcDPIYn49Moq2NEruo3RJGm59PgvqZz1A7EQwOMgLA4eC5JUM5FjMVIgcHqd69/jrvbd06jtvn46K99lqOrbnZZDiGQqYOS20ts2Hr6ngOr5faQjBIU1QwyDDd8nKO5fXX+b2nxyT6yDOqqmJm+Z/9WUrUgL+YwnslYJvOnQB+AuBxPZ6jvG+SxjgrcdVVzOf5l38xwgVgpMZIhFmzksBpYe7BZqNvcVa2Oh0dpUmlsHB8JFB6uulPsGkTcxGOHTM2fXHsejzkhpKbUFjIRbB9Ownh7bfT9v7EE3TUSYr80aMk9JIIl1zoTvo6JBImRFbyKDo6mERSVsZjtWYOgsfDfQ4d4j779tFBHInQxm+38x7S06mFSA6C+Ay6ungfdXWMzJLqs1IY7K67jA+joYEMxuvleSoryejEtvjOd5LJeL0mfls0pcpK+i1uuCElGMOZcFafg9b6CwAaAPwYwF0AmpRS/zhWyhta631TOsJZgE9+koJMsqk2kTAZ9ocPA5/4xMyNz4KF00ISNZLjrqVzW1eXKWS3ahUJpfR5HRigX6G+nkQuEiHxFkJeUEBzS08PiXBWFiVt0SJKS0lY/X4TKmq389hgkPtLUa7kSopi2pJqrFLDqKuLTu5XXyXzaG83scbS4KeqyvRn8PtNiQOtqSVUVhrG1NNjSgm7XMasBpBhXXoptaT8fEZpve99JPjLlplG621tDM2VqKa1a6ktNDbOikqd5+Rz0FprpVQ3gG4AMQB5AB5QSj2ptf7MVA5wNiAri47Jm24yaymZUcRiLP++aNEcKqtgYW7A7SbxC4f5NxqlHf74cUrYW7bQPNLfT0YgKejd3bSlbtjAST8yQglJQkGFmEciJPZOJyXvggIS9OZmU2CrqorEMpGgNtHba5hKODy+oFlh4XhHtFRpfPVVk7xWWGg6w0mjkJ4eXkN8G04n943FOI5rr6VP5N57ea1Vq4xWZbezmUh+Pom+281zVFaaAn8tLTx3Tw81hI4OaiJNTTxPfT2zouNxk9md4jgrc1BKfQqsyOoF8N8A/k5rHVVK2QA0AZj3zAGgefav/5oJUsnOaUE0CnzpS5wfGzdO+/AsWDgV8TiJmXRjk5rq8TgJ5aJFJIy7dpGYrVtHplFRwQkt2dHRKBmIdF7yeEhUEwlK6lJiAyChLiiguaq11Thsh4d5zZoa05C8sJASfSLB84ujOzvblNpwOHiu3l4yHyHmSnG/RILniMV4vhUreD81NcbPcc01DCnNyuI+NhvH09trSpJ3dpo2qZdeaio2Ske8Y8dojsrK4v00NvIa5eVmfKOjZBgp7ogWnIvmkA/g9rGkuDegtU4opW6Z7AEppb4E4MMA+sY2/cNYb4eUx9e/zjnywAPjG8eIEDU6ymqlr78+R2LgLcxexOO0wbe20v6fmWmK5xUXm1DL7GzT4jItjQxgyRIS3KNHyUDq68lcDh0iY5CIjPJyY7KJRrlvZyevXVHBfUZGyAiEiEq1xauvpulp2zYeY7dzLMuW0TyTvIAaGkjoDxwwBbpycsz4Cwp4DyUlNAG96U2MWIpGgZtv5v+SkLRoERdxbi7HKb1wbTYyCen81NZGv4oU4Ssvp7lqZMRETtXVGYYj4b2zhDEA58ActNZfPMNvByd3OG/gO1rrb0/RuacMSjHxbe9ezj2Ago20nJRAjIcfZpE+C6mP3btpNs7JYWj9LDAVnxv6+sgYDhwwFU8zM2keSU9nBEVFBc0/ra0kgF4vo3bkU13NpLe0NKaL19TwYQHAlVeSkQwNceKvWcNFEQjwGmlpdBQDZA7V1STqAwM890038aGvXcuqh+3tpsqly2V8IrGYyV+w2zmG/fsNk2hoIAH/+MfpxJZIkbe+deLn0tBAjaCzk9cIBnkPS5bwOsePM3orN9c0dpFuePG4aUYkGeTCWIBZJxFaeQ6TjPR04J//mVFvEoknBeOcTm675x7OfSt7OnXR08OS4y++yPWelka68fd/Tzo4q+tm+f0M79y2jTdSVMS/ktBWXk5Cd+QI1VyAhLWlhWYY6ap0+eUm4iY9nWVoS0rIADweSu3JWczFxTze5yPxlN7LHg/NM34/+0dUVpIBRCJkKNLtzeHgeHbsMA1/AI5V6r9Lwx9pqnPppfze38/zng1ZWSy//OyzZFQOB8e0YgU1pV27eI82G5+HNPk4fJhMVlogDg4aM5vLZVoRCiTmPYW7wqXqyD6hlPoLANsB/O0ZcizuBnA3AFSnkLr2lreQOfzv/5rmNXa7KfG9Zw/DW7///VknTMwLjIywVPcf/mB8q0rRCvPVr/IdvvnNE1eGTXls3crqkS0tJGZKUSpeutTc7PHjJOBSvkLqC4kTF6DJRIhtMEh1+dlnSZzT02nKWbAAePvbjRRkt5/aw1kwkaTU20vtIBjkuNra+P+RI3TqvvnNJK7Hj3Osl19OppFI8BpSKiMnh8xnxYrTvzStyWykJtKKFaYAYEEBn4c0ASouJsHPzeV9Dw2ZUFapwHniBBnw2rXUii691EiHR45QE5LGJUuWkPmlGGaEOSilngJQOsFPnwfwAwBfBctyfBWsCPvBic6jtb4HwD0Ak+CmZLAXiJ/9jHPg0CEyBWllmZ7O77/7HTXtj31spkdq4WQ8/DAZw8nluGMx0ofXXuOaLyubkeFdOPx+4Cc/ISGWpIxolP6CQIDEOBAwhaGkeJ4klImPQBzNsRgJ97ZtJIYdHcZWH4sZAnmhLRJDIZ5jcNAwhmiURPzYMV5340ZK8u3t/F3yNaSns/SMEA4/Efr76T8IhXjfBw6QaK9bx2dy5AgXb3W1aTwutd3dbjIeu93UV5KIJNFmDhygdnbppSYJLz+f4xkc5H1cdlnKaREzMhqt9XXnsp9S6kcwfatnFdxuZka/972cd+npJmgjkaD2+Y1vsGHNyfXBLMwc/H4K1hP1aQC4ljs6aJKedczh0CES94oK4+CV7knd3SSMo6OmhrlUFBRp2OMxFVlHRlhAzOvlfkNDNK+IBCwlJY4cufDa5R4PH7bLxeuWlvKvz8exSxvPoiIyhcFBMormZjLARYt4T729JNbCHKJREvgTJ8h0Tpzgb5mZvJYk+I2M8FlUVBhne2kp71l8Eh4PmUZBAa/Z1WU0jXic+9vtLKWQns5jpbsVQAbS28vtpRPJyzOHlFOMlVLJS+42ALM2ya62Fvh//4/CRkaGKakhwRx+P/Dtb5vtFmYW0g98//7T72Ozcf339k7fuCYNyck3mZmmUXZmJglXYSGJ67Fjpjd0NMrfJVlNymK0t5Owac0H8uKLtMknF5OLx005jguB3U67fUeHSX4T9VuS5gYGTDZyfr6JSKqs5LV7e7lNJLB4nNLakSM874kTdLinpQF5eYgePIre5gB6ehUiR1rHNzWXJuXLl5PxhcM894oV1KTy8zm25P7S0SgZZSzGrO2JFrvDYfw4yQgGeV9S2XOakVp6DPEtpdRq0KzUAuAjMzqai8SVVzKAQjoUSrCEJGfu2sX5mVwqxsLMoL+fFpbR0dPvk5lJU/Zrr5kAGZuNNKy0NMX9EEuXmkqnUilUqpLabLyBhQtJ4DMzTS/kRMJ0XVu+3FQbDQRoEmlt5fmOHeP5pD9qMMhzXmjdGHEG2+1sOCSOZql62t9Pk012NvdbvZrXrKnhfYyMcL9kf0Zfn+ltfewYtY7+fqC9Hd4TI9j5mguJQS9QoKHyFVZGj6NsQxXHsHEjGdHAAK+5fDnzFvbs4SKWmu8+H8fa20tGNDzMMZeXM6phyRI+P8n0jkQYDgzwuFiMyXOtrSbUsbiY15pGJ2XKMQet9Zyq1eRyAe94B7XSSITzPZEwzukTJ9j34667UpywzAMcO8Z3JD3BJzItlZXRFB0KAU89Rd9Dfj5DXqurU7xvdF4eJ9qPf0zC2dnJm1yzhr/5fJycIi2HwySKDQ1MUCspGd/J7fHHaVJyOrlfTg41io4OEkC3m5E/F0rQcnPJWDZtIkGWPtOJBBdWby9NR8uWmXLamZlcVAsXTtzvYXCQx2rNT3o6kJmJ6IEm7BoqhyczinSVAJx+xNLS8frrGrn2A3BftZGMpKjINDlvaQGef96YoAYGTAmOjg4yErEje718viMjHOeTT5KR5uSY9qpFRWQYPh/fTW2taT/o9VIzk/DfaUDKMYe5iCuv5FzYupX/S7i29GX/6U85p9/97lkeIjnLEYlQiKyp4XdJrBVaWVzMgJiCAkMHBgdpkna5jAaY0iHKb3oTCepDD5maRn19lPylXERuLonUggVkHH193FZaSvNMaysnc3OzyVSW0t35+SSAb3qTST47EyRKSLqtJRejs9vJiXfsYJmObduMmai+njHFwSDHEIlwzC6XYWwTRQBlZBjzVGHhG85hvx+IBUNId4eBgnzAnQGHTgDRGAbiOahYupTHHTxomsi3tJgkt/p6XjstjWP66U+pWdntplWo2837bWoy3fFKSsgMnniC51q+nHkUwjBiMb6nZcs4wRYvnjYp0mIO04C6OhL+oSG+X6k4kFwS/1vfooBwxRUzPdr5ieZmvhuvl4y6uppr3O/n+3K7WX6nsZH/FxebrHfABMOMjKQ4cwBI5K+4ghPP5SIx2r+fkzItjTfi8fBBFBdTc/D7SaiLikzJCKfTqFpCGKuqjCN3ZIRMpKKCmsnJCIVoV/X7zQNcunR8FnF2NqUrv58vQGyz4pDeutV0ZgM47pwck7ehFK8t2ktpKYnz8DDH6vcDvb3QVdVQbW4gb6wFaGYmx+8oA4oTJga9t9eU9QgGyVwkciszk9f3+0nE+/qMD0aYgVLcVl1tEgulnLPXSyYRDPJ8vb08v3TFy842pRdCIdMCNSODjHySMzQt5jANKCxkIqlE9jU3c34XFnIeS6WC//5v06fdwvRB+r/U19OsvG+f0f7z8rje8/P5CQap7Q8O8nsyHdM65aswE4EACdFLL5HYSyE5p5ME98QJ3syqVbSXhcOcrM8/T+IdiXCi2u3szpaXx+9Hj5IwOhzMeWhooES0ZQtw3XWmT4JIvvv2kchJ9I44bZNrJwHcfyLmUlhI841ERiUSJKjSNU6uozUJfkYGF9yGDbTzSkOehgbkHm6GLbcSowk/0sZKgMRG49C2BPKWlHKcXV0mYTAW43M4cYLXqK7mc2pqMsl+AO/DbjdNjLKy+BkZ4QQqLzfF2Pr6TMJfIEBmHY1y/44OOr5/9StKJD6fcbQHAnSWrVkzqSF0FnOYBihlkkclQVQpzoFwmIJYOMz19LvfsRy8hamH1lxzv/+9Ke+zYoXR6AcHKQD6fBSapX1ya6uhacXFPI/PR7qTTNNSCkNDHHgkQklb+jAcPUqJuLDQdCkDTME9m41MIi+PD6ilhUQ4K4s2tsxMOoWliJ5SZDKSd1Bfz+33388HFIuRwFZV8feKCjNGqZja1WUe5JlyFIJBEvdIxBDf+nqOu6fHNBuSzOY1a3ictAIF3sgQd9bUYI19D3a9kkC81wtlswE52Vh5ZQYyli0w0UQyDuke53abbZIJLVJCRgbvRepNBQJkTNJ03m7nPgMD/G1khM/TZuPfeJySi/TByM3lPYlGIrkV0hfj0CGTvT0JsJjDNEFs1h/+MIWx3l7ObSkbL0EKn/4019D118/0iOc2QiH6Ux99lAm20oyssJDm3aoqvoeiIjKI4mL+1t9POqu16fkCUGBbsiRFfUb79lHqiMVMP4brr+cNhMP8vb2dhCo9nYRHErKkNIQ018nJoXYQDJpoIum3IEX00tJI/IaGKOUeP05Cl51NQp6fz2sPDxuns0BKcIdCDDft7jYJaHV14xPFJGu7osIk9R0+bPpdyzny801CmsfDCJAFC/gBeO/d3SjKcOLKpf0Y7I5Al5Uh97Z1cC8sM/4MgH8lYiE3l9JFcTGfh2hYhw7xeUnRweFhXktMBQsX8hkeOGCepcfD568UrydhwKEQn2VmpimQmMyA2tvNu/H7+bxdrkmZNhZzmGYsWgTccQfwgx9QUJN+IvIJBoH/83+owU9UZcDCxUNrCmPPPEPBz2YjTYtEKBh3dpLe3HEH17PQrrQ0rsOyMgpva9caZ3WKJbcaDA9TNZJG9xIN8eKLJCbNzeR4PT0kxJEIiV00SsIkDMHhMLWA3G5yzGeeMUX5Cgpoz08kyAykoxtAm518t9spTWdlURs5cIAVDQXBIBfJa6+RQBYUmHOGQiZENTnhTorbxWK8n7Q0jsXp5PfhYWOrDQRM21HJg3j9dX48HqTn56O0qhQItwO7XgZCS0iAlTI5FZWVfCaZmWQQPh/PU1trpIMjR0wlWLfb+Gbe9jbamBMJ+he8Xj6H7dvZMyIcNsxIenUL04jHeR0JQ5ZzRiKm9PMkhrqm6pSes3C5gDvvpHDzgx8YE2xyglxzM/DFLwL/9m8pTHRmMQIBPv9QiDTx8GGue0lOlBbEe/fSEpFIkNaJgBwKkdbNinfT2kpCI4RUEtrCYaqw4m8YHTXNdaSfdE4OCVJOjjEJtbRQnTpwgBJ5fT2J78svk6sWFlJjyMnhdY8eNQ9uaMhcY9cuqtES/eN2cwxVVUbiFl+E1GR6+WUuDiGO2dnUKFpaeF+BAM+dl2eyugXy0sRxLQ7gI0eMpC4hrlKT6ZlnOOZVq4w5rqPDdKqT/SUiSxzWZWUmf0RCcKNRMpLHHycBv+QS+mGOHOF5V6/meNrb+V4kXNbpNCYol4v3KzWa4nETRtzfT4YtiVSTgNkwveccCguBz3+e2bhdXeMr/UpY9AMPMCzy05+eJURoFiEe5xoSJjE4aARPgNtFqC4rY0CMUhRGKytJm5KF3ZTGyXYuKdcwNERiIwS0qMjYyBIJTkiHgw9KpPAXXjDS6YEDfBhisqmupjNWQlJ9Pk70YJCEfXiYBFyqq/b3U/1asoSEtrub55JjTvbsS3e4hgYTlTM4yGtt2mSk+6VLyTSk9IeUIli92tyPOJH37uVkSCRMDwin00Q/NTeTaSUXHFy4kMeMjDDEVhokFRbyGQwMkCFXVjJxZnCQv8t+fj+dx9IcacUKhsDJub/5TUMMHA7zHIJBPi+xfy5YQMaUkWH6ZNfWTsaMeQMW2ZkhZGcD738/8E//ZOaCrFOJIrz/fjpCr746RW3ZsxRZWSZKrL3dJCQmd/Dr6KDlZd8+mrqdTgp5dXVs2DRRflVKQlp3hkIkdA4HbWZS/yMcJiHr76cJJDubBMzpNI1y1q0jERoaMpm9Hg/PIT0Lent5/r4+Tt5160y5ie5uk1U4OEhGkZdHZgOQSK5dy+NfecW0+xR1bWCAdtaBAdrzFy7kWKUntSTBac0IrHic5wsESISbmjimvj4SfAlttds5/t5eU5YjGuXLLyszDmCvlwxl7Vou1qNHeX1hNlLivKaG+/X38xzip5BQ33DYaEWtraakt0j7dXUk8BkZxmci5qJwmNfWmuNfvpxMpaRkfATYJMJiDjOIv/5rmjrvu8+YTdPSOCc9Hs6NRx7hvF60aOJoPgvnD7udloLnnjN5CqHQ+H0iEWp1fX1cp1KodN8+ZkbX1/M8Wk+qJj/58HiAW2/lRBodNcTmIx9h0bnnnzdOE4lCWrjQTMiGBkonv/61aXUpBLSqioQzFDK5BRkZlG7b2kjA1q9nGJ5ULw0GjUlkcJATOxSik3jhQuPMTk8noW1oIEPo6uJvUmzvkktIGE9uubhmDWurSz2irCzgL/6C1x8eJpFdt47jPniQTGjxYv7f3c17lM5zAgkrDId5bZeL9wsY0490gJOY55YW7q+U0dBEI5OaVQLZJlFNck/p6ZyI4lvIz6dksmHDFE4YA4s5zCCcTuCHP6REeuDAG5n8iES4btLTaaLs7WXU4eWXGyHOwsWhuJjWiF/+kgwg2e8DGP8fQCEvkSAtysykufx3v+NaTiRokZFGYSmJpUupHciNVFdTAlmyhATzj38kgcrIMKGUZWUkRo2NJPQ9PSSwklUsUUjBIIlrfz8J3rp1JgksM5PXrq9nxnBhIa8Ri5lPWhqv4/WSUa1dy4dfV8dtUmZCCKaEgb76KpPjysrG210zM7lQhob4AqWnM8D9pOpqaSnHc/Ag723xYi5Cn48mm9JSLsCeHkOkRTuShDYp3x0KmaikJUv4fGIxTix5ViUlZBY2GzWiyko+p/37OVZpQ1pebnpbSw0pYSxvecu0JkFZzGGGMTLC2ktDQ6bJVjBoBLfRUTIPv5+fo0fZP2XOtKucIUgwSH29aVGQbFaSenOJhKnK0NdHupWezjV95ZWkdX4//RKXXZbCSXBSmuHkbXfdxT4Dv/wliTFAzpmbyxvLz6eq1N/PmxUJvLSUhNPh4EecxGKuSSRIcEdG+KC2bePkra+nLV66xXV30xxz6BCZUDzORXD0KLfv3s3zl5eTOA8Pk4i2tDDqZ/HiU+9VKSPZJ0NC02IxM+7GRr548XNceSXHvGDBeDOZ1JbKzSVD8Xo5vmiU35Xisxoe5nPu7eXzTfZpiP9DMiu3buU1i4q4/8MPUyOrrzf+G615nspKq/DefEMgQG1682Zq3yKACRFyubhNwqF7emgnf9vbztzYysKZ4fMZP+rICJ+plMxxOvlckzV/YRReL+mE1H8TOtTXx/V9Lp0oUw6LFgF/9VckVqGQKecgmc6HDple0N3dxixUVGQc1rINMM5dib232/nAli/nb14vf/N4aCaSFptSHkIigPbv5zlDITKM/Hyq1A4Hx7x69fnF9Dsc1GT27OG1laLk1dVFwisRQceOmRLg8uLf8x7eQ1eXkRQ6OnhPUsxPEvJ6emi3DIX47KT0RVYWGczhw5yAkQjNY4EAmUFpKZ/XkiUcr6ilbjfPrTWZ5DTBYg7TjHjcFL/MyeGckjDwSy9leHdPjwlEkDL2It2K9v/HP3Jur1w503c0u7FhA2nGyAjpXyhkTMLSNsBm43abjUJdRQWfvdttzuN0knnPOkgWbjBIx67Y1sJh4Ec/4k3n5Jg6QkNDJLCiNZSV8Texi/t8fDBS1iE/30jHbW30U9hsdDzH4yR6nZ3cFgzyrzi7+/po+hkZoSO5ttbkSdxyy/gXcK6oquIC2r+f0UqSc9HaarSZzEzeXyzGBXrVVcZ5LE2E2tuZP5KbS2lBfC67dvEchYU8d2UlJ43PZwoLdnbymaenm+Q8yckoLOQ7uPxyjrGkhL9VVXGxT2MKvsUcphF+P6PfxHwhAtLrr5P4ZGebdShRbwDXqXRm1JqmUSnuuGDBLCj0loKQ3i0VFXy+paX0iR4/bqI4pZac3U4at3gxmUl7O+lDsqlbqi/PKoyOckL6fCZ2PjOT0uyWLSSWtbWcYH6/6QIXDnPiSVZvIkEiGAyS0G3ebJK5GhspKY+O8u+OHZzo2dkkkMkMJDubRFkarovpRjrBDQyYHIST6y+dD/LyOOYVK8zCkgSzl1/mPaSnm3ubyJRTVkaiLxnLgsFBMsaKCk6w7m4+P6mVJP0lpLRGUxOfscNhEt4WLyYjbWggI160iNumuejajDEHpdS7AHwJwFIAG7TW25N++xyADwGIA/ik1vrxGRnkJCIeZxCFJJwC1Dibm7k2pDKv328i16QHizQGkoxqmXMA/XcWczh/OJ18djt38vmHQlyL2dlGSxMLxubNpE9Ll1J4W7GCdES0jKEh0qmiopm+q/NEWxsHn9y20uejqUUqD0qIp3jj29v5AC6/nA+ns5PluaWb2fAwnbTd3dz/gQf4sOvrKekPDvLcUsQqL4+MY+tWEtysLD5c4ciJhCn9HY3y/8JCU+TO4+H1HA7aZ88lpE9UxOJijjEc5vm7uoz5zOPhiy8vp5mppma8NGC3c0Ls3m0YxPAwJ4HkckiZjIwMMj3ZLxTi84zF+A4WL6aJKj2dzykvj8dUVzNSbIbC4WZSc9gH4HYAP0zeqJRqBHAngGUAygE8pZRapLWOT/8QJw9DQ5yD2dn8PxqlIJSbS6FCmm+tWGECGNLSyFTkIz1FZO7+4Q8kWl/5iqlOYOHcUVhIi4HkKR08SBq1Z48R+KSDZkMDE1rr6sisu7vpE43F+FtVVYqHtE6Ejo5T1Z2cHBLJujrekCSvSahrRQU1C8meltaccvN+vzEftbVRLU4k6JCWDOi+Pko6UkDM5SKhHxw0WoPTSaI5PMzFkJlpWptu3EhV77HHTBtScfy+9a2m50R/P19ibe2pWoYkDlVV0a+yaxdfqttNAr14sUk4CwRI0E+WwiSyqKXFlPa45BJqR4ODHG9WFp+D9LiWEuJSME9KbEjUg2Rxx2KM/JrBSTVjzEFrfRAA1KnZXW8HcJ/WOgLguFLqKIANAF6Z3hFOLqQGj0B8d0rR/7RjB9fB4OD4xEeRTiUeX7L8JRt/61aGu9566ywkTimAtDSzNvfupR9xyxY+b6eTf1tb+ayl8oJSpsbSrIaYMpLNIpI+vmQJH0ZTE6UYkVDWrTMSvddLSfnQIT6UtDSGo772GjUMm40PLi+P+4jkLepwSwsl8upqOtwGBznpL7uMk/zZZ0mkOzupTUh9KAkDPXrUhOVKVvM3v8kY5cxMnl+ihm69lYQ7Lc2Et4bDXGCdnWQAEmkleQ79/by/5EzlkyF5Dcm47DJee2CAeReXXUaJ49VXyVTz8kx70CuvZJ8KMWv19PAeKypmXOJLRZ9DBYBXk/5vH9t2CpRSdwO4GwCqkwvrpyCysw2RT0ujNCrBHCtWcK20t3NNlJVxrkhSZyw2vq9xNGq6lmnNRNM1a0xhSgvnD/HDSkRldjbpXSLBd3DwIIXS84EEpBQWpijjrqkh0UpPNw4wn4+Sc3o6k8deeIFmlfR0StVi/5d6SWI+8vloo9uzh+cJBExhu6wsSuGSpZ2ezv+PHSNnLioic9ixg+eVzGVp7lNWZsaQkUFm0NRkujA1NZkoq7Y2vrDychLtoiIyjd/+lufeuJHnX7GCzGfbNo61pITM5/hxnkeqwb7wAnDTTedn78/IMCUxBNXVZApiGwZI/DdsMEmI0hI0RTClzEEp9RSA0gl++rzW+pGLPb/W+h4A9wDA+vXr9Vl2n1FIdeOdO40WkZ/POScVDDIzuV6HhkxwiLS5ldpgdrvpgihdBHfsYI0w0XItnD8kAXHrVjIGh8OExCd3sjxZA5wIgQAtHk1N/N/jYf7SokVTfx/nhfJySifbt5t+0tIK1OvlQ3nzmymhP/64aWhRWEifQ18fCbfNRknZbueklXIdUmSvq4sPVzKHhQh2dpqeyx6P6U43OkqinpfH/48cIdHOyzOp6cPDpiSFJJ25XDw2EuF28ROUlPBabW18CdLVzeEgUwsEKAVIMlssxutKoqBsu5giZy4XNYj2dj5bMSuJnTkFMaXMQWt93QUc1gGgKun/yrFtsx5FRdQiRUuXBlpdXZx/N95IbaCz05SycThM/xTANKGSKgiJBP1x995L5rN+/Yze4qyGmIDF2jI4yL/i+zl8mHRnxYozJyE+/DDfiVRwDgRYJ+vuu8f7fmccEqaalWVqG7W3s1TG0qXcnpZGjinS/OgouV5np8kylr7JPh+lYpm0UoPebucEVorEXfpGrF1rQkQBMpCT1V+x361dy9olYrdfuZKLpbeXjECc5aEQr+/1khmUl5sidrEYmcrRo6YKrGgq0lI0uUyBw8GX191NJnGxqrnLxfuvr7+480wTUtGs9CiAe5VS/wo6pBsAbJ3ZIU0eXK5TNUeZc7m51ALq6rh+ensZXglwfougFIuZOkxuNwWjlhbga19jvSYxuVo4P2Rn0zz3xz+aZluhkBFk9+/nPtu2UcidqIyJ10shV/rIAKZI6Z49dGqnDCQeWprF9PUZbSAc5kT8058ozeTkcNLl55s8hMFBk+EsTjKlSPD7+031U7GLSvnhQIC+hPr6c08pz8oyIbIy+fPzge99j9fyennumhreQyTCmO8TJ0xykNRAikZp9mlpIdHv6ODvGRk8TyxGzl5SwnuNRvnyJGJrnmAmQ1lvA/CfAIoA/F4ptVtrfaPWer9S6n4ABwDEAHx8tkcqnSvKyzkvm5qM2fcDH6DZU9aVCGAulxHsJPt/1y42CsrPBz75SeD2261qrucDCb/XmvRFGLGY8V55hbSiooKCc34+t0trYtlvomcufcJTClLWQUwb0tJSzEIACbH4DySySQh6ZiaJb00NPzt2GAld2lmGwzxnfb2xi2Zk0PsvNY4kiczvp/d/dNRoJSfbSZP/X7qUNe3/8R/pLCoq4tjEDiiZ1eL7kIqwQ0Mct93Oay9bxvHIPaanUz0cGeExZWW8V2l7Ok8wk9FKDwF46DS/fR3A16d3RDMPpbjGKisprEgi3DvewaZR+/ZxLb/yiuk94HRScBoZMWuvvx/4+MepdXzpSylmykhhCE1sbTX1rQRDQ3RKS9KczUb6pRRpUXExg3skuCUSGe/DHBpKQboiZS2CQRI/qQcSCpk2hEVFtPlLJULxI2Rmkqh6vab89+bNlFi6urgtLc2En4ZCptSt3U7JvK6O2195hQ9zyxaahtraOIkbGpgJfeWVE0fuSAKQpLLHYryfqipTjmN4mL8VFvKvMK+BARNb7vGQAWzeTC2ipcX0gi0ro2ov1WTnEVLRrDTvIS1DAbMmKipYqv7gQc7dlhZKrsGgyVdyuTj/JeT70Ue5dj/+cfoPUzJiJoWQSPAZSxMugdYUZvv7qcUdPAjcfPP40iVS86q2lsEtjz5qimr6fKQvE9WIm3EsWcIQS4mNPnSIE0tsn0VFJLbBoOkqFwjw5v1+4IYbSHjFXr9zJ/eTmkw2Gwm030/GoRQjkwAyiupq/vZf/8WH3NLCh19aSkK9ZQuPv/VW4/OQLOPjx+kPWbjQ1H2SNn4lJeTY0lK0r4+LSSrB7t3L6xw9Sie108nv2dl8iZmZdOKJc0kc8fMIFnOYJcjMZM7Qxo00F917L9dNRweJkOQ/BIMmPj8SYSDKpz9NYvbBD1rhrmfCiRN8nsnVWU/G8DD/vvQSBdqiItIbyXWqrSVNKSigmToQYKLd0qXTWlDz3JGbyyia1lbjeZcs3kCAZpa776aX/dgx2tMcDt5cTo7JUg6HSdjb26nmSlKbFOfLyOB3cfS63WQ04TDLBEjzG8mN8PvJSNraSLRfeME42EpKTM2lwkJqCStWmNhvsQdKf4R43LT/FNue2001XZhHfT2vuX49X17HWAyMZFOXlc27pu4Wc5hFEJNoTg7wmc+wUORvfgP84hckWhKXD3B9OJ1ch4ODrMj82GMUwP7yL2dp9dAphtCVM/lppAjfiROsS3fDDab/TVYW86zcbtK55ECclEZyOe9EwhTYy8gw/RvS0qhl5OeTSMbjfAjLlwP/+7+U0o8fpzZw9KiJUAL4gLxeTkbpttbQQML/2mv8iEYi5b59PtP0R3Iw6urISI4d4xgGB6mFSHVE6SERj5ucDGnQMzxsuthJPaX8fCb1RSLk6tnZZALLlvFcXV0ce3GxMUvNI1jMYZZCgkI+9Smup3//d2NeFdOo2815H41S2AqFGK6+axfwiU8wMmeeacpnhMNhzNjiS0iGVGKVYqBtbYxc2rTJ1Ezbvp30ataa8MQ+nzwxjhzhjY2MkNAePWqqEu7bZ0pLZGYaD356Os+htTER1dVxW24u44Klv4LUP+/rM5EXUhJDKlEqZRrulJfze2cnNYr6elPqW2oxXXcdmdcrr3CbtB49dMjUVLLb+VfiwpOJf1bWvC9aZjGHWQ6nkwxi4ULgG9+g8AZwzns8pirA6CjXWDxOwWvvXgpJt93GyKaUbVIzjUgk+NxcLtI4sUYIXC7SMUmGczopYLe2UqhuaCDtHBiYhUX4TodEgk4WrU3C2eioqfzo9dK+JklpAIm+mHWGhgwxV4r2/USChF2ymKWr1ZYtZBA+H8+TlcXthYWU6ru6eO7+fhL24mJy9KYmmoNyckxORFMTX8yGDWREkj26cyfPPTzM2OSREeNjCIXIyCwAsJjDnIDNxu5wN90EPPQQTUjHjpkex+KHk6oGEuHn9wNf/jLwq18BX/gCGcU805zHweslgXe5yDwlPF/6RKenG2d/Q4Op/FBXRyuECKCzJqilp4daQChkbuTkqKCuLkO8pUSFJLjZ7STkzz7L2iJiqnG7TfluIbqrV7Mcx8KFhmEIBx0aIqHesIFMZNcubk9PN3bUzk7T3ESynwsLqSW0tpJpLV/O62RkkLEMDJiCdoWFLAJYUkLtQ/o6P/88x7poEe9/cJCaksdD2+s8btxuMYc5hPR04M47WZLmW9/iGguHuT7E1Do6akrUy//79tHn+M1v8u+dd854za9ph7QDbWw0Tcza2oxZLjPTBMosW0bfQlMTaVNZmfFTSE2slMeJE8apnJ1tQlIvu8z0ed63jzfZ1cUb7+426lQwyP2k+mB3NwnrwoUmbM7jMclkS5fSaSw1mkQbUcpkHx48SCK9caMxWR05Qid3IGAcaJmZpjhdNEqmIlFGkpnocpGwDw4aDcTvN4lysZiJsnI4eK6tW41j3m7ntk2byDTmISzmMAdRVESN4KmngHvuYaJobi4FOOkHAZDwSQmOUIj7fe5zwI9/TCbx7nfPEkI3CdCa9FF8n4sWkV55vaQ3V15Jk93goOkVU11t+sKEQhSAKypSulwOkUiQ6BYUGKexlJ09cYJq0LFjZAo1NXwI0i1tYMCkfC9ZYopQJcftbtrEh5hIkHnU1jIJRBI/3G6mkB87xodls/HhrV5Nk9DICAvlSfe0cNiUz5XopNxcaj55eaZKq5i1hoepafT08Fxi4vL5+IKlgUpFBc1YiYSpf79wIffp6qK/4sgR5losX35hnedmMSzmMEeRkcE+029+M/Czn7EKglQJlb4sgLGpiw1dep58+9t0tr7nPVyvE5WKmEs4fNiY4NatI/0JBChI33wz6YjWpJ3SW0Y0hu5u0pfVq01iXEpBMoZFBQL4/eTYWmnGI/H/wSDVJ7FH1tbS3i+p5FJDqaSEDMHhIBGtrTX2NXEGnwxpxN3aSm2ksdF0T3M6ScA3bybxDoWo4TidVNsee4zEPy+PE/nQIV5XCvXt3MlxFRdTfe7s5LmleqzEKm/dSubhcpnezVlZDMn1+YwGJJVaL700ReORpwYWc5jjcDpZguPSS5mY9YMfmB7WyYxBCk5KlE0sxqjBRx6hsLhxo2l3O9cQiZBGLVvGoJqODtImp5MaRMVYwXilqC2cXB0+pUNWxYYvzSgARvdIpnBypdFQiDc7Oko10u0mt3Q4uG3tWv7f3k5i6nCQSWRmmvZ5mZlGNT0TIVWKnLS0lOM4fpwOaa3JCCSLORjkOHw+Si27d/MFFBaapt1SCymRYEKfMBSbjZqP08mJu24dxzkyQu7v8VDNloZBu3dTS+rrM9fMzKSGFQqZUuDzBBZzmAdwOEj4GhoY1PHlL5sIRAlHFxohwqCU3Y/HqV3/8pdcJ4WFdH7PJW1idNQ4k6uqqBFIz4xp7Oc+Ndi/n1J/cTGJZyxGU0lFBblgVhZvVqqZVlcbW5o4YqTxfXs77Y1OJ/C731FKj0aN40qa+pyPFKE1iXJfn8lG7uoiQysoMCaggQGToV1RQQJfUsJJ7fdTo5H69eXlHH9rK9Xf9HRqAfn5LBWwZYthXJEIr9PczDFkZ/NYt5v3J2G6UpPJYg4W5iLS0phlfcUVNLH+67/SeiBCnzS8crtNv4njx00+lMPBdfzsszQhr1nDpLrZnlDX3s4w/tJSWhbcbt6r5E3NWkheQF4ezUOdnSYaITeXRPbppzkBcnJMvoDXS6nh0CFTTyk7mwS2vZ3MoKGB5pfmZjKO0lJOnpYWU0zvXOD3kygnFwCrqKBEImGtAwO8jsvFiSjd3Pr6eB8+H7/39/P6nZ0mdtvhIDO8/npqEX4/JaXjx02lWenzHI9zQWRk8JmJpCCRGye3VJ3jsJjDPITLxRIcb3sb8NxzLGuzdSsFL2EMFRUUuPr6SPylt8HoKNfqli0U7h58kGarO+6YfSanvj4yt/37TXBNYyPvJzeX9KekZKZHeREQ22FrKx0jOTmmMuP27XQKX3aZURcDAUre0mZwzRoyA4lYEk/75s0kyIcPG2kiFuMEGBigo/lcmcPpUtJrakjUpQViXh4ZiPhCbDYyvq4uw8BkDL29vAdJcovHjZYzMMAF4PXyfgYHTdG+sjLaEe12agjV1XwmAwOcDHMmeeXcYDGHeQyHg4mk113H9X/ffaQZ8TjXRVaWCQCRvi0DA6Z4pzhk77+f2rf0pK+t5fGpmiXc1wf81V+RsQmk2OiePdz+1a+Sds5q/6OUkHj9df49cYI3JE3K/X7jUAHIHbu6uD0apVYhpa0BEtI1a6g+BgI8h8fDCZOeTu2koMBEO5wLXK5TU9EB06UtP5/Xl/LZJSVG3W1uNn6L+nqOSRJUgkGqvLm5ZBDd3RxbVhY1nb17ea8S0RQK8X76+sgwKyv5PSODE0GK+80jzK+7tXBaNDQA/+//0ZSyZw/Xnd3O0HPA5CGNjnLtSQSiy0VB7dVXyRzcbh6/dy/XXE8P19V11818JKDWpHd33WXqqgkkvyoQMJ345kTW+MKFrH2ktWmjmZHBFyhEfHiYmoKUrZCGN6Oj5uN08oW//DIJdFoapXmfj8wkEjE+ig0b+BBlnzNJCTk5lMh7e43Pob2dhLmiguc9dowvrLLSRBYtWMAxbtxIf8fQkEm4y8uj5C+tRcW01tdnNJGiIjKMaNQk5A0N8d737aOEs2kTnWvztPeuxRwsjENWFgWnjRspfLlcpC1S6kbK3JSUmGxhgIQ/keC248eBBx4w4aBST23RIq63yy+n1aG62gTDTAViMQqbDgdpUEsLkwP7+0+/fyxmSvnPCcTjNMfk5fHmMjNJtFtbyThaWmhXa28nIY5E+HtZGY/zekk8fT4S1hMn+GKLi8ksmpv5V0LggkHTrlNrXm/9+tNHLyjFGODjx6l5AGREq1aRKUjnttpavkxxXIfDppWpMIKqKk5MqbMkHbOOH6dqLA74ri5qETKZpahWXp5p2LFxIxlQysUlTx9mjDkopd4F4EsAlgLYoLXePra9FsBBAIfHdn1Va/3RmRjjfIYQ1Ntuo1D14IM0KaWnk6A7nVzvo6NkDEVF/K2ryySaSqJtLEZasWsXIyTvu4/recEC5mFcfTXpVCTC9Xv8OL+LgFtYaHrTS79tgNfMzib9GxoiDSku5rk6O1lkUEzK1dUUkCXRTZL/TobkRiVbW2Y1/H5y5b4+43MIBkkkFy6kTbC52WQF5+fzobW2GqIpvVIlskkpfpqaSPzLy00GshTRE/u8z8eXfqbm5g4HVdeaGjKWxx7ji83Pp18jM5MTzunkmJcsIdOQXtF9fdxvzx6OVdr5bd7MiSETVsp8i7YyMEAmMTRkSnXYbFSBs7PnNWMAZlZz2AfgdgA/nOC3Y1rr1dM7HAsTQSlGN11+Odfdn/7EDGrJHZJidA0NXIP79hlhUnpKiJlb8q5Es5eudtnZpgz29u08R2sr6UpWFmlEezv/l0Y8Hg/X9dKlFCpraihs9vaahkiSiJtIcPu+fRyvhPhPBLeb/oZU9ZecN6SPQmGhKUFdUcGHkJVluLDEKdtstAWWlBibu83GF5iWRgKan8+/XV3UMMQvUF/P8w0MmOvn5hpzzunsdFKR9bXXTDaz38/fCgpMF7q6OmoHfX3GzPXkk7zmyIjh+PE47+/JJ422FI1y8oijOhQioxkc5CTKy+O1AgEyu/lWP2YCzBhz0FofBAA1z7nzbIFSpBO3305m8fLLJOaSESxBIQ4H16nTaRJRZb1KGWxx/mptzE7PPUfaceLE+OhJgP5UCacXRiOlQPbv57gWLuSxjY1kMP39plObzUb6lZnJv6LRJCcCAvztscfOLOTOOojJRXo2SKhqRQUlbckd8PvJFMrKjLOovJxcVpqDiONXcgGGhvjSFi0yeQUSPioQE8/pEIkwQuroUR6fk8MXpTU5tWg0LhdfbmYm99OaL7m01JQeltowPT2m+1skYgoKimPb5eK9SYcmaV0q8duXXDJv/QzJSFWfwwKl1C4AQwC+oLV+YaKdlFJ3A7gbAKpPTlu1MGUoKmIinEB8hu3tRprfts30bREiLIlmUu5f1mMsZkzW8ThpkSTb9vSYns1iBnI4uF8oRJrg9fJ/n88QfdFOBA4HhWGxeuzYYYJyMjKAD3+Y/og5F5CSlUVut2+faWNXUkLC+uyzpoeClL/eu5eEdPly2uIKCqgJSGlrj4dmnUCAL0hqnNvt/K2paXy7Qb/fOLAnwuHDpoJrSQkZwsCAYVJacwzS43poCLjmGl4nHKbkn5FBIu/zcVJI9mZ6uslfADhZ8vJ4TikA+Na3knlWVPA6DQ3GMT7PMaVLQSn1FICJCqR/Xmv9yGkO6wJQrbXuV0qtA/CwUmqZ1nro5B211vcAuAcA1q9fP1dciLMO6ekU6hob+f+SJWxNunevcWQDJmpRqi44HCZxNRQi7RJmIl3tkhmLaB6ibAqzkBI+Eq5fUGACboTYi6n59ttpYtq9m3Rl7Vq28ZzTCmxhIfCmN/EhB4NUtySaSArrSRG7SIQMZfNmEuj163mcNOfJzjYvddEiU2xPVLm3vpXX7Ovjy8rKorQwEaSQV0EBX5DXSykgHKbzaeNGMoxrrzXVIaUMeH+/MYdlZHCySZmP9HSOU0pvCEZHTZN1iarQmpJOchKeBQBTzBy01tddwDERAJGx7zuUUscALAKwfZKHZ2GKsHw56zj9/vf0UWzfbuiHzcZ1K0R/+XKu78svp6lKtAqJhMrMnDi5Tkzg0tRodJSRjtKvJTOTAqGYkKJRVlYtL+d+a9dO+2OZWcRiZArd3TQnOZ0mXFRqKCUSJlJASnEDfAEVFbQn+v2mSmtREZmKMJqFC7lteJiEOS2NqtrpmoQkEiTybW0877Fj3C69FoJBcu7kaq6CvDwylNFRUxxQohIAUzvd5eK95eVxLPn5wI030nSUSBgpxcIpSLmnopQqAjCgtY4rpRYCaADQPMPDsnCecLuBd76TH4BWiN5e0pGWFhLx4mKasGtqTMmKgQHSHgmv93go4Hm93CccNgKkUqRjl1/OdS+C5Jo1pEtNTUyydbnIhKR//bzE4cOMFIhGSXSrq/kAh4dNcS273ZSMELVLbPtVVfy/vZ3/K0U/wfLlZBqSI5FInFuLzXic6tuOHVThXC6OT46tq6OJJxg0Y4nFaK988UUypT17jENcQumGh7mvRCfZbFQZXS7+3bSJUsKcSGKZWsxkKOttAP4TQBGA3yuldmutbwTwJgBfUUpFASQAfFRrPXCGU1mYBfB4+Fm4kMR8IqxeTZOUlAES05CUz3n5ZdKmzExTRLCxkTTA4xlvcgLYW2bFimm4uVRHLMZey5EICaiUySguJrGVQlLSB1o0BFHvKiqYmLJ7N6Xz4mITfrZjB7n3wICx5S9dSmIciRhTlEBeUm8vTUFFReTq3d3cPjDA/5cvpxYzMMBr5uUx4ebRRykhdHZyohw5wusVFJgersXFPJ/PRzXR7aYUcs01ZDoWYzgnzGS00kMAHppg+4MAHjz1CAvzAS4XTdmLFp362513nvnYOe03uBgMDpLT5uQw0mdoiLY+8eSnpZGgFhXxBRw+zMbkACVwt5smHCl0d+wYifvwMM83OkoVrrCQ57j3XjKUoSFTnOvSS83/onUcPEhtJSPDOJXy88npjx2j1pCfz+1tbUy28XrJMGw2MrLhYUoMS5caxrJ6NcddXEwGU1LCe7cmyHkh5cxKFixYmGT4/aacttYm4SMSMU7j0VGai0IhSuW1tVTxamt5Dqm6uGULnchDQyTGr79Oort9u4leKiqiViClsg8fpuaydi1VPcnKFqdSIMA4ZKlZIj2fOzpMVdUHH2QWpZi/pH6SZHxnZDCb8sQJModFiywN4SJhMQcLFuY6hEgrZcpwSwMc6fEwMkKi29nJv9u30wewbh1www2m7/Prr9Osc+AAmcPoqNE4JL74+HES8auvJoEeGiJDknjj3l7aBXfuNG06h4dNT4nKSu4nxa6GhmgmkgxKrU2pj9FRMggptCeagsUYLhrz1T1nwcL8QXGxiRmW+H6HgyYiyRweHCQBlsJSTied0Pv3k6FI/+WeHjKNoSETBiZmnkSCBLutjefbtcs4kCTiKC3NFL2SqrHSe0EijCS1Xvo2KMXvRUW8hhTMstlMXPTwME1ZtbVzpwvVDMPSHCxYmOuQFplPPklJ3O8nYXY6ja1fMgfjcdrnm5tJZHNyTEwwQIIsPRwkKigUIoOQ1prhMH8LBBhRNDJiiuVlZPAzOEgm0dBgmNLwMD9iYsrMZBNzpcgk1qzhOfx+nltajVZU0IwkBfqkT7aFi4KlOViwMNchzW5uuonhYJWVNB0NDZnMQYfDFK1KS+O2UIgOYIfDVFmUmkwZGSYfwe02qe1uNwm01tQG/H4eNzJivkuJbq1N/SaPh4ynr8+UvejuZpVGm41MwOlkfsLKlSyktXQpk/vWr6dPZfFik4lp4aJhaQ4WLMx1iDS/eDEdvVLLKBIxNU2km5uYlEIhk5VcXU3TVFUVj83IoJTe20uiHouZqKfSUmoD/f00J+XlkcEsWcI45uZmjuPaaw0jKC6m0zkSMTHPlZVkXu3t9E0sWcLr9PYyHLW0lO0+Fy82yXQrVszjRJbJh8UcLFiY6xCpPhJhPsDVV9PUdOIECbM4hKVaqZTUVYoMoaGBGsNtt9EpvXOnSTUXE9LKlaaUxoIFprKr1oxuWraMJqq0NG6//HJGLW3dSg1jdJRjKC01GcsSjhoM0j+yYYNxrHd3k6FJDfbGxrMn3lk4L1jMwYKFuYy+PuD559kCz+FgMlhaGp3M1dUmEsjjIXFeuJCE1unkvqtXmwqleXnAl74E/OY3JOrp6STea9cy67ivD3joIVOW1+UiYb/6aia1+Xw85oorTLOg3l6OS5LaAJMNHY1S01m4kOdPhjjCEwkyjpluMzgHYTEHCxbmKgIB4Le/ZcTRyAhNL7t3m8if3FxqEqOjpnRGebnJV2hoODUbMTMT+OAHgXe9i8Te4+F5RMu4/npmXYvje9UqSvxSa11MVAAJf0MDHd4rVzK6ye+nBhCLmaznjRtPvTenkxqIhSmDxRwsWJirePVV4IknKKEXFpIot7fTb5BIkPhKM/DOTtYpKi3l9vXrSbhP1/VoovpJNhvNR3V1NDcNDbHA1eAgfy8qGl/PRCn6EqqqyEzq6oDf/c7USCkuZuG9uropeTwWzgyLOViwMFcgGoDdzuzil18mkZZSFseO8a/Xa0pfuFz0EUSjJOJLlpBhPPssy+quXk2CXlR07uUnklsEVlTQtGS3nz7EVDSVkhJqGlKZsaqKx1tO5hmBxRwsWJitGB2lzb6jg9E+vb00x0iznepqag9a01wj+QjJOQ2Dg8Z5XF9vml1ItFJHB7Ohr7mGWsH5Irml37kgP99qtpMisJiDBQuzESMj7Lk8OAg884ypi5SXRx+Dw0HHb0kJHb1a85hIxJTPkM5LgMlR6O2lD2D1akrskrm8ezdrq1u9lecNLH3NgoXZiMOHTSkJv5+mmMOHGWoq2kBPD+siFRaScYTDdEanp5seB1lZZAqStyA9oKU3g2gQYoqyMG9gaQ4WLMw2JBIk/IWFDEk9eJCag93OnIKREe7X20uTzqWXkuDX1tJMFApRa5CG22L2CQZNLaPRUfoopKWe1oxOSu4QZ2FOYyab/fwzgLcCGAVwDMAHtNa+sd8+B+BDAOIAPqm1fnymxmnBQspBKTKCSIQ+BL+fWoFkOweDJPp9fdyvqIiMQcphRKOm8U80SkbgdJJZ7NzJv36/SXQbGOA5HnmE4aYVFcyFKCqa6SdhYQoxk5rDkwA+p7WOKaW+CeBzAP5eKdUI4E4AywCUA3hKKbVIax2fwbFasJA6UIrEfvdu04gHoHSfSFDCz8ykVvG+9zE0NZEwTXoaGpgdnUjQn9DfTzNTd7fpphaJ0IzU18fje3rIgMJhaictLazVZOUazFnMmM9Ba/2E1jo29u+rACrHvr8dwH1a64jW+jiAowA2zMQYLVhIWSxcyFDPYNAUy4tGSdhdLmoMbjcT0vr7jVlIKTqWKyp4jNRVkobdeXkmJ6KoiHkPra1kQLm55nevl1qG1jP9JCxMEVLF5/BBAL8Z+14BMgtB+9i2U6CUuhvA3QBQXV09leOzYCG1YLcDmzaxKY/PR78DYKqr2mzcp62NJqO8PCaXSYRSdTXNSs3Npvfz4CAZRjDIcx09So1iYIDhpV4vmZLdTgbU00Nm5HTO2GOwMHWYUs1BKfWUUmrfBJ+3J+3zeQAxAL863/Nrre/RWq/XWq8vsuyfFuYjLr+cTuJQiEQ9EDClt3NyTIntQMA05enqIqHv7qZGMDpKIj86aspe2Gw0OeXn8xzJTm6A+zscJhTWwpzDlL5ZrfV1Z/pdKXUXgFsAXKv1G/ppB4CqpN0qx7ZZsGDhZGRnM+u5qoolMEIhEveMDDKJaNREJ8XjrJV0+LBpvxkOm8glYRA5Odze0EANQ5LlMjPJHKqq+P8NN5x71rSFWYcZ8zkopW4C8BkAb9NajyT99CiAO5VS6UqpBQAaAGydiTFasJDyaG+nliC9oKW1ps9HTaG4mA1yNmxgGYzhYfZAyMzkMdI2VHwPAM1GOTnMmC4oYGRSfj6d1P39ZD7XX89mOxbmLGZSJ/wugHQATypKH69qrT+qtd6vlLofwAHQ3PRxK1LJgoXTIBIxWcyVlSTyUj8pHqfzOBwm49iwAXjqKfoLSku5j81mWoVKf4bFi/l9cNBENK1dS8Zyww3A5s1W74R5gBljDlrr+jP89nUAX5/G4ViwMDtRU0NJvqCAYafp6fwUFJAJ9PXRFLRkCXsqHDnC7dnZ/BuPk0lkZLD6aVoaezVrzeqt0ks6Kwt485tZY8kyJc0LWN4kCxZmMxYuZFXVQ4dIxJUiga+roymooQG47jqTj7BpE3DvvdQY0tPJWGpraUaqq6PWceONPMcVVxjndFkZI54szBtYzMGChdkMhwO4+27ge9+jn2F4mPkIABnC1VePT1SrrwduvZXd4YaH6a+Ix3lMIEDtQKqiejx0PluYl7CYgwULsx2NjcD73w9s2UKCH41Sm7j11okzmFeupMbR1sZPJEIz05IlVu0kC2/AYg4WLMx2KMVWmitX0oksjXbO1CTH4yFTaWyctmFamF2wmIMFC3MFbrcJR7Vg4SJh9XOwYMGCBQunwGIOFixYsGDhFFjMwYIFCxYsnAKLOViwYMGChVNgMQcLFixYsHAKlJ4jzTqUUn0AWmd6HOeIQgDemR7EBWI2jx2Y3eO3xj4zmOtjr9Fan9LzYM4wh9kEpdR2rfX6mR7HhWA2jx2Y3eO3xj4zmK9jt8xKFixYsGDhFFjMwYIFCxYsnAKLOcwM7pnpAVwEZvPYgdk9fmvsM4N5OXbL52DBggULFk6BpTlYsGDBgoVTYDEHCxYsWLBwCizmMI1QSr1LKbVfKZVQSq1P2l6rlAoppXaPff5rJsc5EU439rHfPqeUOqqUOqyUunGmxnguUEp9SSnVkfSsb57pMZ0NSqmbxp7tUaXUZ2d6POcDpVSLUmrv2LPePtPjOROUUj9RSvUqpfYlbctXSj2plGoa+5uS7fBOM/aLmusWc5he7ANwO4DnJ/jtmNZ69djno9M8rnPBhGNXSjUCuBPAMgA3Afi+Uso+/cM7L3wn6Vn/YaYHcyaMPcvvAXgzgEYAfzb2zGcTrh571qmeK/AzcA4n47MAntZaNwB4euz/VMTPcOrYgYuY6xZzmEZorQ9qrQ/P9DguBGcY+9sB3Ke1jmitjwM4CmDD9I5uTmMDgKNa62at9SiA+8BnbmGSobV+HsDASZvfDuB/xr7/D4Bbp3NM54rTjP2iYDGH1MECpdQupdQWpdQVMz2Y80AFgBNJ/7ePbUtlfEIptWdMFU9JM0ESZuPzTYYG8IRSaodS6u6ZHswFoERr3TX2vRvABH1XUxoXPNct5jDJUEo9pZTaN8HnTNJeF4BqrfUaAH8D4F6lVPb0jNjgAseecjjLffwAQB2A1eBz/5eZHOs8wOVa67WgWezjSqk3zfSALhSacf+zKfb/oua61SZ0kqG1vu4CjokAiIx936GUOgZgEYBpdeBdyNgBdACoSvq/cmzbjOFc70Mp9SMAj03xcC4WKfd8zwda646xv71KqYdAM9lEPrdURY9Sqkxr3aWUKgPQO9MDOldorXvk+4XMdUtzSAEopYrEiauUWgigAUDzzI7qnPEogDuVUulKqQXg2LfO8JhOi7EFLrgNdLSnMrYBaFBKLVBKpYHO/0dneEznBKVUplIqS74DuAGp/7xPxqMA3j/2/f0AHpnBsZwXLnauW5rDNEIpdRuA/wRQBOD3SqndWusbAbwJwFeUUlEACQAf1VpPqnPpYnG6sWut9yul7gdwAEAMwMe11vGZHOtZ8C2l1GrQPNAC4CMzOpqzQGsdU0p9AsDjAOwAfqK13j/DwzpXlAB4SCkFkNbcq7X+08wO6fRQSv0awFUACpVS7QC+COAbAO5XSn0IbAnw7pkb4elxmrFfdTFz3SqfYcGCBQsWToFlVrJgwYIFC6fAYg4WLFiwYOEUWMzBggULFiycAos5WLBgwYKFU2AxBwsWLFiwcAos5mDBwiRBKXXrLCyKZ8HChLCYgwULk4dbwcqpFizMeljMwYKFc4BS6pKxAmausczf/Uqp5Um/XwrgbQD+eax2fp1S6pNKqQNjx903tt+XxoqgPaeUalZKfTLpHO9VSm0dO/6Hs6D0uYU5DCsJzoKFc4RS6msAXADcANq11v900u8/A/CY1vqBsf87ASzQWkeUUrlaa59S6ktgGYmrAWQBOAygFEA9gG8BuF1rHVVKfR/Aq1rrn0/P3VmwMB5W+QwLFs4dXwFrHYUBfPIs+wLAHgC/Uko9DODhpO2/l2KLSqlesMzEtQDWAdg2Vm7CjVlU5M3C3IPFHCxYOHcUAPAAcAJwKaX+AcBbAEBrvXqC/d8C1s16K4DPK6VWjG2PJO0TB9ehAvA/WuvPTc3QLVg4P1g+BwsWzh0/BPD/APwKwDe11p+XFoxjvw+DpiIopWwAqrTWzwL4ewA5/397d2iDUAwGcfyOBUgQWHZhhidQLMAKTIElWMISD8kILPPEIVpB8kHAgPr/dNuk6tJe0qoFyzujpMH2ss9f2F79ZhvAZ5wcgC/Y3kqakpx7UXyzvU5yfRp2kXTsJfNG0sn2XO1UcOidw8v1k9xt79V+TZtJmiTt1F4CBf6OQhoAUHCtBAAoCAcAQEE4AAAKwgEAUBAOAICCcAAAFIQDAKB4APmdibP8SYajAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization(data, test_recovery.detach().numpy(), 'tsne')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization(data, test_recovery.detach().numpy(), 'pca')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "trainings_loops.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "torch",
   "language": "python",
   "name": "torch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
